The Philippine Statistician

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Record ID: 160 [ Page 1 of 4, No. 1 ]

Modelling Portfolio Risk and Diversification Effects of a Portfolio Using the Exponential Distribution – Bivariate Archimedean Gumbel Copula Model

Authors: Owen Jakata and Delson Chikobvu

Abstract:

This study uses the Archimedean Gumbel copula model to construct the dependence structure and joint probability distributions using the Exponential Distribution as the marginal distribution to asset returns. The main objective of this study is to estimate the diversification effects of investing in a portfolio consisting of two financial assets, viz: the South African Industrial and Financial Indices. The Exponential Distribution is used as the marginal distribution of the returns, instead of the Normal distribution, to better characterise the financial returns of the two assets. The scatterplots indicate that the dependence in gains, as well as the losses are better captured using the Archimedean Gumbel copula. Monte Carlo simulation of an equally weighted portfolio of the two financial assets is used to model and quantify the risk of the resultant portfolio. The results confirm that there are benefits in diversification, since the riskiness of the portfolio is less than the sum of the risk of the two financial assets. It is less risky to invest in diversified portfolios that includes assets from the two different industries/stock markets. Due to dependence and contagion between Global stock markets, the findings of this study are useful information for the local and international investors seeking a portfolio which include developing countries’ stock market Indices containing, say the South African financial assets. This study provides investors with a framework to quantify diversification effects, which allows for the avoidance of extreme risks, whilst benefiting from extreme gains.

Keywords: Expected Shortfall. Monte Carlo simulation, Value-at-Risk

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Year: 2023 Vol.: 72 No.: 1

Record ID: 159 [ Page 1 of 4, No. 2 ]

Local Quadratic Regression: Maximizing Performance via a Modified PRESS** for Bandwidths Selection

Authors: E. Edionwe and O. Eguasa

Abstract:

In the application of nonparametric regression model, it is a well-established fact that the bandwidth - also called smoothing parameter- is the single most crucial parameter that determines the quality of the estimated responses that are obtained from the regression procedure, and that its choice (how small or large the size) is hugely influenced by the criterion that is applied for its selection. Under small-sample settings, which is typical of response surface studies, the penalized Prediction Error Sum of Squares (PRESS**) criterion is recommended for selecting this all-important parameter. However, for the purpose of selecting bandwidths of improved statistical properties, we propose a modified version of the PRESS** criterion specifically for Local Quadratic Regression (LQR) model. Results from simulated data as well as those from two popular problems from the literature show that LQR procedure that utilizes the bandwidths selected via the proposed modified criterion performs outstandingly better than its counterpart that utilizes bandwidths selected via PRESS** criterion.

Keywords: Desirability Function, Hat matrix, Penalized Prediction Error Sum of Squares, Response Surface Methodology

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Year: 2023 Vol.: 72 No.: 1

Record ID: 158 [ Page 1 of 4, No. 3 ]

Spatiotemporal Patterns of COVID-19 Cases in Quezon City, Philippines

Authors: Tricia Janylle B. Sta. Maria, Nancy E. Añez-Tandang, and Edrun R. Gayosa

Abstract:

Various studies have been undertaken to explore the spatial characteristics of the COVID-19 pandemic. However, only a few have considered the pandemic's temporal characteristics to assess space-time dynamics. This study focuses on COVID-19 spatiotemporal patterns in Quezon City, Philippines from November 2020 to October 2021. Spatial clustering and spatiotemporal patterns were analyzed based on a space-time cube (STC). Results showed that hot spots and cold spots were found in the city's northern and southern parts, respectively. Also, a significant increasing pattern was revealed throughout the study period. Moreover, STC analysis demonstrated that intensifying hot spots or locations that were statistically significant hot spots for 90% of the study period and the intensity of clustering of high counts of COVID-19 cases is significantly increasing overall, was primarily concentrated in the center and northern regions of Quezon City, where the majority of the barangays in Districts 2, 5, and 6 are located. Barangays identified with this pattern were Bagong Silangan, Batasan Hills, Commonwealth, Holy Spirit, Payatas, Matandang Balara, Pasong Putik Proper, Fairview, Pasong Tamo, and Sauyo. As there is a possible resurgence in COVID-19 cases, identifying spatiotemporal trends and clustering patterns is vital for regulating and controlling COVID-19's spread. Thus, the study's findings and methods can be utilized to predict and manage epidemics and help decision-makers control existing and future outbreaks.

Keywords: spatial clustering, spatiotemporal analysis, space-time cube, coronavirus

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Year: 2023 Vol.: 72 No.: 1

Record ID: 157 [ Page 1 of 4, No. 4 ]

Utilization of Machine Learning, Government-Based and Non-Conventional Indicators for Property Value Prediction in the Philippines

Authors: Gabriel Isaac L. Ramolete, Bryan Bramaskara, Dustin A. Reyes, and Adrienne Heinrich

Abstract:

Property appraisal and value estimation in the Philippines are prone to human errors and bias, due to price subjectivity and the general difficulty in properly quantifying the impact of factors beyond the property itself. Predictive models for property valuation typically involve conventional features of the house (e.g., number of bathrooms) and market prices of nearby properties. This paper investigates the value of incorporating alternative data to account for deviations in true market value and improve property value predictions in the Philippines and other developing countries. The study considers public data and anchors socio-economic indicators to assess its relevance to property value prediction in the Philippines. By utilizing the Department of Trade and Industry’s 2021 National Competitiveness Index Rating, this research also investigates the significance of a Local Government Unit’s competitiveness based on their economic dynamism, government efficiency, infrastructure, and resiliency. Different commonly used Machine Learning (ML) methods and features from various data sources are compared and it is found that the inclusion of government indicators has substantial positive effect on the model performance on top of conventional indicators that can be globally replicated. A Mean Average Percentage Error (MAPE) of 10.7-21% is obtained which is competitive compared to the performance ranges of other reported models. A property segment (personalized) approach is proposed to achieve lower error rates in Philippine appraisal (in 87.5% of cases), better access and transparency for populations outside the real estate network, and minimally biased assessments, all of which are also relevant for other developing countries.

Keywords: property appraisal, spatial analysis, city competitiveness, clustering

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Year: 2023 Vol.: 72 No.: 1

Record ID: 156 [ Page 1 of 4, No. 5 ]

Verification of Coffee Product Form and Determination of Conversion Rate From Coffee Dried Berries to Green Coffee Beans (GCB)

Authors: Dennis S. Mapa, Ph.D., Divina Gracia L. Del Prado, Ph.D., Vivian R. Ilarina, Rachel C. Lacsa, Manuela S. Nalugon, Abella A. Regala, Marivic C. De Luna, and Ray Francis B. De Castro

Abstract:

The Philippine Statistics Authority (PSA) collects, generates, and releases production data on coffee in the form of dried berries from the results of the Crops Production Survey. In the computation of the Supply Utilization Accounts and Food Balance Sheet for coffee, the coffee form used is green coffee beans (GCB), using a conversion rate of 28 percent from dried berries to GCB. However, the Food and Agriculture Organization of the United Nations (FAO) and International Coffee Organization (ICO) use a 50 percent conversion rate from dried berries to GCB. The common form of coffee traded by farmers and the conversion rate from dried berries to GCB were investigated through consultations with traders, processors, and other stakeholders; and surveys with coffee farmers and traders as respondents. The results of this study show that the common form of coffee traded by farmers is GCB, and the average conversion rate from dried berries to GCB is 50 percent.

Keywords: fresh berries, field visits, survey, processors, experiment, percent recovery

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Year: 2022 Vol.: 71 No.: 2

Record ID: 155 [ Page 1 of 4, No. 6 ]

Determination of Dry Rubber Content of Rubber Cup Lump

Authors: Claire Nova O. Abdulatip and Honey Fe G. Boje

Abstract:

The quality of latex from rubber trees is determined by the amount of Dry Rubber Content (DRC). Mainly, the price of the cup lumps is directly dependent on the DRC, commonly determined through visual observation by rubber dealers. Thus, no standard method is used by rubber buyers in the industry for farm gate determination of cup lump DRC. But the Philippines used a 25% conversion rate from cup lumps to dry rubber; however, other member countries used 50%. Therefore, this paper addresses the conversion rate of dry rubber content of rubber cup lumps in the Philippines. “On-site validation” was conducted by collecting data from selected rubber processing plants in major rubber producing provinces, namely: Zamboanga Sibugay, North Cotabato, and Bukidnon. Secondary data, such as cup lumps and crumb rubber volume from 2017 to 2019, were collected and analyzed using Percentage Ratio Comparison. Results indicated that the DRC of cup lumps to dry rubber was more than 50%.

Keywords: Rubber, Conversion Rate, On-Site Validation

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Year: 2022 Vol.: 71 No.: 2

Record ID: 154 [ Page 1 of 4, No. 7 ]

Topic Identification and Classification of GooglePlay Store Reviews

Authors: Daniel David M. Pamplona

Abstract:

Digital distribution platforms, such as Google®3 Play Store, contain an enormous quantity of information related to app data and user reviews. A particularly challenging task is to classify a large unstructured dataset into smaller clusters or topics. With this, data from 19,886 user reviews was extracted from Google Play Store. The main task is to determine app characteristics, though common themes, that are commonly mentioned in positive and negative reviews. Text data was preprocessed and then common topics were identified using LDA for positive reviews and negative reviews. The accuracy of topics was assessed using perplexity-based approach and human interpretation. To further validate the topic model, the topic assignment was used as the outcome variable in Naive Bayes model with reviews as input. Empirical results show that the extracted topics can be predicted well using text reviews. Finally, the distribution of topics was calculated according to different app categories.

Keywords: Topic Modeling, Latent Dirichlet Allocation, Naive Bayes Classifier, Perplexity

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Year: 2022 Vol.: 71 No.: 2

Record ID: 153 [ Page 1 of 4, No. 8 ]

A Bayesian Hierarchical Model for COVID-19 Cases in Mindanao Philippines

Authors: Jejemae D. Nacion and Bernadette F. Tubo

Abstract:

A Bayesian hierarchical modelling approach is utilized to nowcast COVID-19 cases in Mindanao, Philippines for the year 2020 to 2021. A spatio-temporal model is considered and the proposed methodology explores the possibility of a flexible way of correcting the time and space delayed reports of the COVID-19 cases for a duration of 4 weeks for the 27 provinces in Mindanao via a Bayesian approach. The goal of the modelling approach is to include parameters that will correct reporting delays in the dataset and derive a model using the Integrated Nested Laplace Approximation (INLA). The study shows that the proposed model was able to capture the increasing trend of the COVID-19 disease counts, that is, the prediction counts derived are closer to the true count compared to the currently reported counts of COVID-19 cases which showed a decreasing behavior. The ability of the proposed model to nowcast statistically significant estimates, particularly, for epidemic counts of COVD-19 in the presence of report delays may aid health authorities to have effective control measures and issuance of warnings to the public.

Keywords: Bayesian inference, spatio-temporal model, reporting delay, nowcasting

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Year: 2022 Vol.: 71 No.: 2

Record ID: 152 [ Page 1 of 4, No. 9 ]

Hierarchical Bayesian Model for Correcting Reporting Delays in Dengue Counts

Authors: Mikee T. Demecillo and Bernadette F. Tubo

Abstract:

Real-time surveillance and precise case estimation are necessary for situational awareness in order to spot trends and outbreaks and establish efficient control actions. The comprehension of the mechanisms of a sudden rise or fall in disease cases that change over time is hampered by the reporting delays between disease start and case reporting. This study uses a flexible temporal nowcasting model with a Bayesian inference for latent Gaussian models built in R-INLA to rectify reporting delays for weekly dengue surveillance data in Northern Mindanao from 2009 to 2010. Additionally, it seeks to quantify all the uncertainties involved in replacing the missing value. The statistical issue is to forecast run-off triangle numbers based on actual counts ????????!,#. In contrast to the currently reported instances, which seem to be declining, the posterior predictive model on thegiven temporal dataset recognizes the fact that there are more dengue cases than there were previously (supporting the actual scenario). This implies that even with delayed data, the model was still able to provide a reliable estimate of the true number of instances. This paper offers a model for nowcasting to aid in dengue control and good judgment on the part of interested authorities.

Keywords: Latent Gaussian Model, Nowcast, Count Data

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Year: 2022 Vol.: 71 No.: 2

Record ID: 151 [ Page 1 of 4, No. 10 ]

Estimating the Magnitude of the Poor Households in Metro Manila Using the Poisson Regression Model

Authors: Bernadette B. Balamban, Anna Jean C. Pascasio, Driesch Lucien R. Cortel and Maxine R. Ridulme

Abstract:

In the Official Poverty Statistics, Metro Manila, also known as the National Capital Region (NCR), is one of the areas that belong to the least poor cluster – a cluster that has relatively low poverty incidences. The Philippine Statistics Authority released the 2018 Municipal and City Level Poverty Estimates using the Elbers, Lajouw, and Lanjouw (ELL) methodology. The 2018 Small Area Estimates (SAE) of Poverty also released estimates for the 14 sub-areas in Metro Manila, ranging from 1.5 percent to 6.5 percent. The city-level poverty estimates were released as official statistics using direct estimation technique. Given the relatively low poverty incidences of the region, this paper aims to estimate the 2018 poverty incidence for the legislative districts in NCR, including the 14 sub-areas of the City of Manila, using the Poisson regression methodology and to compare with the results of the ELL methodology. Data sources include the 2015 Census of Population, and the merged 2018 Family Income and Expenditure Survey and January 2019 round of the Labor Force Survey. A total of 5 significant indicators were included in the final model. Results show that the Poisson model produced more reliable estimates for NCR than the ELL methodology. These SAE techniques allow for generating more granular poverty statistics useful for targeting poor beneficiaries. Furthermore, information may provide an opportunity for the LGU to act swiftly and provide appropriate subsidies for areas within Metro Manila.

Keywords: poverty statistics, small area estimation, survey and census data, time invariance

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Year: 2022 Vol.: 71 No.: 2

Record ID: 150 [ Page 1 of 4, No. 11 ]

Application of Consecutive Sampling Technique in a Clinical Survey for an Ordered Population: Does it Generate Accurate Statistics?

Authors: Mohamad Adam Bujang, Tg Mohd Ikhwan Tg Abu Bakar Sidik, and Nadiah Sa'at

Abstract:

This study aims to compare the statistical generalizations which are inferred from samples obtained by both systematic sampling and consecutive sampling and then compare both their results with the true population parameters of the target population. This study was conducted using two approaches. The first approach was a comparison between sample statistics and population parameters based on a simulation analysis to estimate the population parameters from three types of statistical distributions (i.e. Normal, Exponential, and Poisson) by using seven sub-samples and 1000 iterations. The second approach was a comparison between sample statistics and population parameters based on real-life data sets which comprise six sub-samples and four parameters. Based on results from the simulation analysis, systematic sampling offers a greater advantage by having a smaller value of mean square error (MSE) in 40 out of 70 comparisons (57.1%) while consecutive sampling has a smaller value of MSE in 29 out of 70 comparisons (41.4%). There is only one MSE comparison that was identical between systematic sampling and consecutive sampling. Based on a validation approach, systematic sampling produced more accurate statistics than consecutive sampling with six out of eight comparisons. In summary, systematic sampling offers a better advantage in terms of accuracy. However, consecutive sampling is still able to generate valid and accurate statistics despite the fact that it is a type of non-probability sampling, especially if a sufficiently large sample size has been obtained for statistical analysis. Therefore, it is recommended that in any situation when it can be difficult to apply a systematic sampling technique for a particular clinical setting, researchers may opt to apply the consecutive technique in the recruitment process as an alternative, with a limitation on making generalizations about the target population.

Keywords: population parameters; sample statistics; systematic sampling.

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Year: 2022 Vol.: 71 No.: 1

Record ID: 149 [ Page 1 of 4, No. 12 ]

On Some Efficient Classes of Estimators Based on Higher Order Moments of an Auxiliary Attribute

Authors: Shashi Bhushan and Anoop Kumar

Abstract:

This paper discusses the problem of estimating the population mean utilizing information on the mean and variance of qualitative characteristics. We introduce some efficient classes of estimators based on higher order moments such as the variance of an auxiliary attribute. The conventional mean estimator, Bhushan and Gupta (2016) estimator, and the traditional regression and ratio estimators proposed by Naik and Gupta (1996) are shown to be the sub-class of the proposed estimators for properly chosen valuations of the described scalars. The effective performance of the suggested estimators has been assessed empirically and theoretically with respect to the contemporary estimators.

Keywords: mean square error, efficiency, qualitative characteristics

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Year: 2022 Vol.: 71 No.: 1

Record ID: 148 [ Page 1 of 4, No. 13 ]

An Application of CATANOVA and Logistic Regression on the Most Prevalent Sexually Transmitted Infection (A Case Study of the University of Nigeria Teaching Hospital)

Authors: Nnaemeka Martin Eze, Oluchukwu Chukwuemeka Asogwa, Samson Offorma Ugwu, Chinonso Michael Eze, Felix Obi Ohanuba, Tobias Ejiofor Ugah

Abstract:

This research focused on the application of CATANOVA and logistic regression on the most prevalent Sexually Transmitted Infection (STI) reported in the University of Nigeria Teaching Hospital from 2010- 2020. A population of 20,704 patients was recorded to have contracted eight(8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain the age group and gender that suffer more from the most prevalent STI. Three-way CATANOVA was computed to ascertain the association among drug prescription, age, and gender of the Gonorrhea patients. A logistic regression model was fitted to predict infertility as an effect of the most prevalent STI. The prevalence analysis showed Gonorrhea infection as the most prevalent STI at 33.08%. A population of 6,850 patients recorded to have contracted Gonorrhea infection from 2010-2020 was employed for the analysis. Two-way CATANOVA cross-classification showed that gender, age, and interaction effects were statistically significant at a 5% significance level. Male (3,752; 54.8%) suffers Gonorrhea infection more than female (3,098;45.2%) and aged 30-39 years (1,946; 28.4%) suffers it more than any other age interval. The interaction effect shows that the rate of contracting Gonorrhea infection by gender differs from one age interval to another. Three-way CATANOVA results showed that drugs prescribed for the treatment of Gonorrhea infection depend on gender and age. Logistic regression results showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.

Keywords: Chi-square test, Prediction, Prevalence

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Year: 2022 Vol.: 71 No.: 1

Record ID: 147 [ Page 1 of 4, No. 14 ]

Analytic Hierarchy Process with Rasch Measurement in the Construction of a Composite Metric of Student Online Learning Readiness Scale

Authors: Joyce DL. Grajo, James Roldan S. Reyes1, Liza N. Comia, Lara Paul A. Ebal, Jared Jorim O. Mendoza, and Mara Sherlin DP. Talento

Abstract:

This paper developed the Online Learning Readiness Composite Scale (OLRCS), a composite measure of student online learning readiness based on five dimensions, namely (1) computer/internet self-efficacy; (2) self-directed learning; (3) learner control; (4) motivation for learning; and (5) online communication self-efficacy. A single metric of online learning readiness has its advantage over its disaggregated dimensions. For one, it allows a summative description of each student which school administrators can use for an effective student targeting toward flexible learning. Rasch Analysis (RA) was performed to come up with an objective measure for each dimension while Analytic Hierarchy Process (AHP) was applied to aggregate the computed Rasch scores of the five dimensions. Three OLRCS have been constructed using weights generated by (1) teacher participants, (2) student participants, and (3) combined student and teacher participants. Results showed that motivation for learning consistently received the highest weight while online communication self-efficacy and computer/internet selfefficacy got low weights among the three OLRCS. Research findings also showed that student participants gave more importance to learner control than self-directed learning, unlike the teacher participants. The difference in the teacher and student perspectives merits detailed attention to optimize the online learning environment and enable individual support. Nevertheless, using cluster analysis, the distribution of students who are ready, undecided, or not ready for online learning is similar to the three constructed OLRCS.

Keywords: multidimensional latent variable; multi-criteria decision analysis; linear aggregation

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Year: 2022 Vol.: 71 No.: 1

Record ID: 146 [ Page 1 of 4, No. 15 ]

Implementing an Effective Survey Operations for a Research and Development Survey in the Philippines

Authors: Ramoncito G. Cambel, Dalisay S. Maligalig, Maurice C. Borromeo, and Ronald R. Roldan Jr., and Clifford B. Lesmoras

Abstract:

Studies have shown that research and development (R&D) is a good driver of economic growth. Policies and programs that are based on good quality data are expected to produce better results. Hence, to formulate and implement policies and programs in R&D, good quality data is vital. A good data support system is also essential in identifying critical areas that need intervention, formulating viable approaches in addressing these issues, and allocating limited resources. In the Philippines, the Department of Science and Technology (DOST) has been conducting the Survey on Research and Development Expenditures and Personnel (R&D Survey) since 2003 so that R&D data and indicators can be compiled. To ensure that good quality R&D data and indicators are achieved, the DOST granted a research fund to the Institute of Statistics (INSTAT) of the University of the Philippines Los Baños (UPLB) in 2018 to further improve the design, conduct and analysis of the R&D Survey. This paper describes the processes that were developed and implemented through this research grant in relation to the dimensions of data quality, namely, relevance, accuracy, timeliness, accessibility, coherence, and comparability. Based on the evaluation of these processes, the paper also recommends further improvement on the survey operations of future rounds of the R&D Survey.

Keywords: Survey on Research and Development Expenditures and Personnel, data quality

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Year: 2022 Vol.: 71 No.: 1

Record ID: 145 [ Page 1 of 4, No. 16 ]

Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm

Authors: Ahmed M. Gad and Nesma M. Darwish

Abstract:

Longitudinal data are not uncommon in many disciplines where repeated measurements on a response variable are collected for each subject. Missing values are unavoidable in longitudinal studies. Missing values could be in the response variable, the covariates or in both. Dropout pattern occurs when some subjects leave the study prematurely. When the probability of missingness depends on the missing value, and may be on the observed values, the missing data mechanism is termed as non-random. Ignoring the missing values in this case leads to biased inferences. In this paper we will handle missing values in covariates using multiple imputations (MI) and the selection model to fit longitudinal data in the presence of nonrandom dropout. The stochastic EM (Expectation-Maximization) algorithm is developed to obtain the model parameter estimates. Also, parameter estimates of the dropout model have been obtained. Standard errors of estimates have been calculated using the developed Monte Carlo method. The proposed approach performance is evaluated through a simulation study. Also, the proposed approach is applied to a real data set.

Keywords: Interstitial Cystitis data; missing covariates; dropout missingness; multiple imputation; selection model; the SEM algorithm.

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Year: 2022 Vol.: 71 No.: 1

Record ID: 144 [ Page 1 of 4, No. 17 ]

Two New Tests for Tail Independence in Extreme Value Models

Authors: Mohammad Bolbolian Ghalibaf

Abstract:

This paper proposes two new tests for tail independence in extreme value models. We use the conditional distribution function (df) of X + Y, given that X + Y > c based approach of Falk and Michel to test for tail independence in extreme value models. We recommend using Cramervon Mises and Anderson-Darling tests for tail independence. Simulations show that the two tests are better than the Kolmogorov-Smirnov test which has good results among the proposed tests by Falk and Michel. Finally, by using two real datasets, we illustrate the application of the two proposed tests as well as the traditional tests of Falk and Michel.

Keywords: extreme value model, tail independence, Copula function, Cramer-von Mises test, Anderson-Darling test, Neyman- Pearson test, Kolmogorov-Smirnov test, Fisher’s ? test, Chi-square goodness-of-fit test

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Year: 2021 Vol.: 70 No.: 2

Record ID: 143 [ Page 1 of 4, No. 18 ]

Time Series Prediction of CO2 Emissions in Saudi Arabia Using ARIMA, GM(1,1), and NGBM(1,1) Models

Authors: Z. F. Althobaiti, and A. Shabri

Abstract:

The investigation of economic aspects of gas emissions in terms of its volume and consequences is very important, given the current increasing trend. Therefore, the prediction of carbon dioxide emissions in Saudi Arabia becomes necessary. This study uses annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The study built the prediction model of CO2 emissions in Saudi Arabia by using Autoregressive Integrated Moving Average (ARIMA), Grey System GM and Nonlinear Grey Bernoulli Model (NGBM), and comparing their efficiency and accuracy based on MAPE metric. The results revealed that Nonlinear Grey Bernoulli Model (NGBM) is more accurate than the other prediction models. The results may be useful to Saudi Arabian government in the development of its future economic policies. As a result, five policy recommendations have been proposed, each of which could play a significant role in the development of acceptable Saudi Arabian climate policies.

Keywords: annual time series data, Autoregressive Integrated Moving Average (ARIMA), CO2 emissions, global warming, Grey Model (GM), Nonlinear Grey Bernoulli Model (NGBM), prediction, Saudi Arabia

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Year: 2021 Vol.: 70 No.: 2

Record ID: 142 [ Page 1 of 4, No. 19 ]

Classes of Estimators under New Calibration Schemes using Non-conventional Measures of Dispersion

Authors: A. Audu, R. Singh, S. Khare, N. S. Dauran

Abstract:

In this paper, we proposed two classes of estimators under two new calibration schemes for a heterogeneous population by incorporating auxiliary information of Non-Conventional Measures of dispersion which are robust against the presence of outlier in the data.Theoretical results are supported by simulation studies conducted on six bivariate populations generated using exponential and normal distributions. The biases and percentage relative efficiencies (PRE) of the proposed and other related estimators in the study were computed and results indicated that the estimators proposed under suggested calibration schemes perform on average more efficiently than conventional unbiased estimator, Rao and Khan (2016) and Nidhi et al. (2017).

Keywords: heterogeneous population, Outliers, Estimators, Robust measures, Population mean

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Year: 2021 Vol.: 70 No.: 2

Record ID: 141 [ Page 1 of 4, No. 20 ]

A New Compound Probability Model Applicable to Count Data

Authors: Showkat Ahmad Dar, Anwar Hassan, Peer Bilal Ahmad and Bilal Ahmad Para

Abstract:

In this paper, we obtained a new model for count data by compounding of Poisson distribution with two parameter Pranav distribution. Important mathematical and statistical properties of the distribution have been derived and discussed. Then, parameter estimation is discussed using maximum likelihood method of estimation. Finally, real data set is analyzed to investigate the suitability of the proposed distribution in modeling count data.

Keywords: Poisson distribution, two parameter Pranav distribution, compound distribution, count data, simulation study, maximum likelihood estimation

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Year: 2021 Vol.: 70 No.: 2

Record ID: 140 [ Page 1 of 4, No. 21 ]

A Modified Ridge Estimator for the Logistic Regression Model

Authors: Mazin M. Alanaz, Nada Nazar Alobaidi and Zakariya Yahya Algamal

Abstract:

The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The logistic regression model is a well-known model in application when the response variable is binary data. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the logistic regression coefficients. To address this problem, a logistic ridge regression model has been proposed by numerous researchers. In this paper, a modified logistic ridge estimator (MLRE) is proposed and derived. The idea behind the MLRE is to get diagonal matrix with small values of diagonal elements that leading to decrease the shrinkage parameter and, therefore, the resultant estimator can be better with small amount of bias. Our Monte Carlo simulation results suggest that the MLRE estimator can bring significant improvement relative to other existing estimators.

Keywords: multicollinearity, ridge estimator, logistic regression model, shrinkage, Monte Carlo simulation

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Year: 2021 Vol.: 70 No.: 2

Record ID: 139 [ Page 1 of 4, No. 22 ]

Modelling the Right-Tail Conditional Expectation and Variance of Various Philippine Stocks Return using the Class of Beta Generalized Pareto Distribution

Authors: Angelo E. Marasigan

Abstract:

A risk measure such as the value-at-risk (VaR) is commonly used by financial institution for capital management and calculation of amount of risk exposure against a loss. The incoherence of VaR leads to the calculation of conditional tail expectation (CTE) for remedy. In this study, formulas for the CTE and the conditional tail variance (CTV) under the class of beta generalized Pareto (bgP) distribution were derived. bgP is used to model the distribution of different scenarios of return of various Philippine stock indices using maximum likelihood estimation due to simulated annealing method with R software, and further used to compute the CTE and CTV of the data of returns according to the specified model. To determine the performance of bgP to model financial data sets, a comparison with its generating distributions which are the (generalized) beta and Pareto models were done. Finally, the method of historical simulation was also done and used to compute the corresponding VaR, CTE, and CTV for comparison to the above method of calculations.

Keywords: risk measure, Beta generalized Pareto distribution, value-atrisk, conditional tail expectation, conditional tail variance, heavy-tailed

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Year: 2021 Vol.: 70 No.: 1

Record ID: 138 [ Page 1 of 4, No. 23 ]

Development of an Alternative Municipal and City Level Competitiveness Index in the Philippines

Authors: Ramoncito G. Cambel and Zita VJ. Albacea

Abstract:

The Philippine Cities and Municipalities Competitiveness Index (CMCI) by the National Competitiveness Council (NCC) measures the local government unitâ€™s competitiveness. This paper presents the results of a study that employed an alternative weighting method for the indicators through a statistical technique called principal component analysis and factor analysis. Moreover, the study utilized both national census data and administrative data from the government. Four factors were extracted from the available data. The four factorsâ€™ compositions are: â€œHousing and Household Characteristics, Financial Institutions and Capacity of Government for Health Services,â€ â€œEstablishments for Tourists Accommodation,â€ Cost of Laborâ€, and â€œCapacity of Local Government to Deliver Services.â€ Unlike the distribution of the NCCâ€™s 2016 competitiveness level of municipalities and cities which is symmetric, the distribution of the proposed alternative competitiveness index is positively skewed. This suggests that only a few municipalities and cities can be considered highly competitive. Moranâ€™s I of 0.3457 proves that there exists positive spatial autocorrelation on the competitiveness level of Philippine municipalities and cities. Municipalities and cities in the Provinces of Bulacan, Pampanga, Cavite, Laguna, Rizal, and Cebu, and also in the National Capital Region are generally identified as those in the highhigh cluster in terms of the proposed alternative 2016 competitiveness level. In contrast, municipalities and cities in the Provinces of Mindoro, Romblon, and Surigao del Norte, and those in the Cordillera Autonomous Region, Ilocos Region, Cagayan Valley Region, and Eastern Visayas Region are generally considered as those in the low-low cluster in terms of the proposed alternative 2016 competitiveness level. Statistical properties of the index such as consistency, accuracy, and precision were then assessed using the bootstrap resampling technique. Results showed that the proposed alternative CMCI is said to be unbiased and mean square error consistent. This indicates the proposed CMCI could be used as an alternative Municipality and City Level Competitiveness Index for the Philippines. This index concentrated on four factors with 33 indicators. These factors are weighted in the index based on their importance in their contribution to the variability of the index.

Keywords: statistical index, spatial analysis, factor analysis

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Year: 2021 Vol.: 70 No.: 1

Record ID: 137 [ Page 1 of 4, No. 24 ]

Time Series Approach for Modelling the Merger and Acquisition Series: An Application to Indian Banking System

Authors: Varun Agiwal and Jitendra Kumar

Abstract:

In time series, present observation not only depend upon own past observation(s) but also involve other explanatory or exogenous variables. These variables are not continuously influence or impact for long run and may be removed or discontinued or merger and acquisition (M&A) because its effect may be reduced due to less significant correlation. The M&A theory is developed when one or more variables are not meet out the required circumstance to survive in the system. To analyze the performance of M&A concept, this study proposes a merged autoregressive (M-AR) model for examining the impact of merger into the parameters as well as acquired series. Bayesian approach is considered for parameter estimation under different loss functions and compared with least square estimator. To test the presence/association of merger series in the acquire series, Bayes factor, full Bayesian significance test and posterior probability based on credible interval are derived. A simulation study and an empirical application of banking indicators for Indian Banks are carried out to evaluate the performance of the proposed model. The study concludes that proposed time series models solved the problems of discontinuity in the series and also able to manage model statistically.

Keywords: autoregressive model, Bayesian inference, merger and acquisition series, Indian bank

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Year: 2021 Vol.: 70 No.: 1

Record ID: 136 [ Page 1 of 4, No. 25 ]

Application of U-statistics in Estimation of Scale Parameter of Bilal Distribution

Authors: R. Maya, M.R. Irshad, and S.P. Arun

Abstract:

In this work, we have considered a new lifetime distribution, the Bilal distribution and derived the best linear unbiased estimator (BLUE) of the scale parameter of the Bilal distribution based on order statistics. We further estimate the scale parameter of Bilal distribution by U-statistics based on best linear functions of order statistics as kernels. The efficiency of the proposed estimator relative to the minimum variance unbiased estimator (MVUE) have also been evaluated.

Keywords: Order statistics, Bilal distribution, best linear unbiased estimator, U-statistics

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Year: 2021 Vol.: 70 No.: 1

Record ID: 135 [ Page 1 of 4, No. 26 ]

Analysis of Randomized Clinical Trial in the Presence of Non-Compliance: Comparison of Causal Models

Authors: Ali Reza Soltanian, Hassan Ahmadinia, and Ghodratollah Roshanaei

Abstract:

Non-compliance is a common deviation from randomized clinical trials protocol. Standard approaches for comparing the effects of drugs in randomized clinical trials in the presence of non-compliance are intention-to-treat, as-treated and per-protocol analysis. Each of these approaches has disadvantages when evaluating the effect of medication in present of non-compliance. The current study compared the accuracy of instrumental variable (IV), intention-to-treat, as-treated and perprotocol technique. We assumed that non-compliance occurred for some patients in the new treatment group only, and independent of the patient outcomes. To compare these techniques, various scenarios were simulated. The MSE value for both PP and IV models changes only under the influence of the value of w (non-compliance ratio). That is, at all values of Î¸ (treatment effect), the MSE of these two models increases with increasing non-compliance ratio, and changing the value of Î¸ does not affect the MSE. The MSE value for the AT model if the non-compliance occurs only in the intervention group, this value changes only under the influence of the w value That is, in this case, in all values of Î¸, the MSE value increases with increasing non-compliance ratio, and changing the value of Î¸ does not affect the MSE. But the MSE value of the ITT model is strongly influenced by the value of Î¸. At low Î¸ values the MSE value of this model is lower than other methods and better estimates the therapeutic effect, and in this case with increasing the w, the MSE value increases very little. But as the Î¸ increases, so does the MSE value, and in this case, as the w increases, the MSE value increases sharply.

Keywords: causal model, non-compliance, randomized clinical trials, simulation

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Year: 2021 Vol.: 70 No.: 1

Record ID: 134 [ Page 1 of 4, No. 27 ]

An Improved Class of Estimators of Population Mean under Simple Random Sampling

Authors: Shashi Bhushan, Anoop Kumar, and Saurabh Singh and Sumit Kumar

Abstract:

In this article, we consider an improved class of estimators of population mean using additional information under simple random sampling (SRS). The expressions of bias and mean square error of the proposed class of estimators are obtained up to first order of approximation. In addition, some well-known estimators have been identified as particular member of the proposed class of estimators. The theoretical results are established and empirical study has been carried out using real and simulated data sets. The findings appear to be rather satisfactory showing better improvement over the existing estimators.

Keywords: simple random sampling, mean square error, efficiency

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Year: 2021 Vol.: 70 No.: 1

Record ID: 133 [ Page 1 of 4, No. 28 ]

A Procedure for the Generation of Small Area Estimates of Philippine Poverty Incidence

Authors: Nelda A. Nacion and Arturo Y. Pacificador

Abstract:

The main purpose of this study is to propose an alternative procedure in the generation of small area estimates of poverty incidence using imputation-like procedures coupled with a calibration of estimates to ensure coherence in the regional estimates. Specifically, this study applied Deterministic Regression Approach, Stochastic Imputation-like procedure similar to Stochastic Regression, and applied the calibration techniques to ensure that the small area estimates conform to the known regional estimates. The difference of this methodology as compared to the ELL is that the error terms used for predictions is based on the empirical distribution of such residuals and thereby a protection against misspecification of the error model. At the same time, the procedure is simpler with available computing resources. In addition, the proposed methodology only utilized data from the census short form which is a 100% percent sample. Thus, eliminating another source of variation as compared to using the census long-form which is collected from a sample of households. This study used the Family Income and Expenditure Survey (FIES) of 2009 and the Census of Population and Housing (CPH form 2) 2010 to come up with reliable estimates of poverty incidence by municipal level. Since the CPH is conducted in the Philippines every 10 years, the CPH 2010 is the latest data that was used. The researcher was able to produce small area estimates of poverty in the Philippines at municipal level by combining survey data with auxiliary data derived from census. The study fitted different models for each region. Considering the results of this paper, the following conclusions were derived: The Stochastic Regression Imputation (SRI) is better to use as compared to Deterministic Regression Imputation (DRI) in attaching income to CPH. The SRI was able to preserve the distribution of 82% of the total number of regions. The DRI was able to preserve only 10.22% of the validation sets. Since the error in fitting the DRI in CPH does not follow a well-known distribution (such as the Normal distribution), the non-parametric way of estimating error was used to generate the errors attached in SRI. The technique is called Kernel Density Estimation (KDE) or the histogram method, which was found to be effective in using the SR. Using the calibration technique achieved municipal estimates that conforms to the regional estimates. The estimates of the poor households in CPH reflects the bottom 30% of the wealth index.

Keywords:

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Year: 2021 Vol.: 70 No.: 1

Record ID: 132 [ Page 1 of 4, No. 29 ]

Using Uncertainty and Sobolâ€™ Sensitivity Analysis Techniques in the Evaluation of a Composite Provincial Level Food Security Index

Authors: Christian P. Umali and Felino P. Lansigan

Abstract:

A composite provincial level food security index can measure food security at the sub-national level which can be helpful in policy making. However, it can be non-robust due to the uncertainties involved in the choice of input factors to be used in its construction, namely: different sources of data, normalization methods, weighting schemes, aggregation systems, and the level of importance placed on the different dimensions of food security i.e. availability, accessibility, utilization, and stability. In this study, uncertainty analysis technique was employed in order to assess the robustness of the index constructed through the conventional approach. Sensitivity analysis was done in order to quantify the influence each factor has in the index building process, identify factors that should be prioritized and can be fixed in the successive development of the index, and determine which levels of factors are responsible for producing the desirable model outcomes. The study had been exploratory in considering the ratio with mean normalization technique and a combination of the additive and geometric aggregation methods as potential inputs in the index construction. In computing for the Sobolâ€™ sensitivity indices, the formulas suggested by Jansen et al. (1994) and Nossent and Bauwens (2012) were investigated and compared under varying sample sizes. The results can provide a more appropriate choice of procedure in computing for the Sobolâ€™ sensitivity indices at an optimum sample size, and likewise insights in the future development of a uniform and more defensible composite provincial level food security index.

Keywords: uncertainty analysis, sensitivity analysis, Sobolâ€™ sensitivity index, Monte Carlo integral, composite index, food security

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Year: 2020 Vol.: 69 No.: 2

Record ID: 131 [ Page 1 of 4, No. 30 ]

Investigation of Factors Contributing to Indigenous Language Decline in Nigeria

Authors: N. A. Ikoba and E. T. Jolayemi

Abstract:

The declining fortunes of some of Nigeriaâ€™s indigenous languages are examined in this paper. A multi-dimensional indigenous language-use questionnaire was constructed to elicit data through a survey carried out in some Nigerian cities. The aim was to acquire relevant data on indigenous language ability and possible causes of language-use decline. The results from the survey showed that there is a low level of indigenous language literacy among most of the languages surveyed. The proportion of language use at home was also seen to be generally low for most of the surveyed languages, below the 70% threshold for virile languages. Several reasons were adduced for the non-transfer of indigenous language ability from parent to children and tests of statistical independence carried out showed that the respondentsâ€™ perception that their language is inferior to English, belief that the child will be limited in school, negligence and inability of parents to speak their heritage language were the major reasons adduced for the decline. A logistic regression analysis of the data also showed that acquisition of language literacy depended on a personâ€™s place of childhood, age, level of education, frequency of use at home and the indigenous language spoken by the personâ€™s mother.

Keywords: indigenous languages, language literacy, test of independence, intergenerational transmission, logistic regression

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Year: 2020 Vol.: 69 No.: 2

Record ID: 130 [ Page 1 of 4, No. 31 ]

Examining the Theoretical Assumption of a Six-fold Structure of Management Competency Sub-scales (MCS)

Authors: Manuelito De Vera Bengo

Abstract:

Management Competency Sub-scales (MCS), in this current inquiry was being constructed under the theoretical assumption of a sixfold structure: self-image; leadership; skills; action; performance; and orientation. However, there is so far no empirical evidence available to support this assumption. Thus, a comprehensive measure is therefore needed to adequately gauge its validity. The aim of this present study was to assess the validity of the categorization of management competencies into the six overarching MCS. The results showed that instead of the expected six-fold structure, MCS comprised eight factors: expertise; self-image; skills; leadership; innovation; influencer, sustainability; and orientation. Looking at homogeneity and scale length in tandem, the scales were subsequently refined and the number of items reduced to 45. The inter-correlations between the derived sub-scales, as well as the mean loadings of the items on the sub-scales, were significant, indicating the validity of the construct. The Cronbach alphas for the different sub-scales were found to be of an acceptable level, above .7 suggesting relative stability of the derived scales. Comparing the inter-scale correlations of the management competencies sub-scales with their average Cronbach alpha, the values were found to be substantially different, providing support for the discriminant validity of the construct. Furthermore, conducting a second-order factor analysis, all the sub-scales loaded above .3 on the one extracted, suggesting convergent validity. The study provides a good alternative to the bounty of competency models/frameworks that have been developed in the area of management competency.

Keywords: management competency sub-scales, factor analysis, validity, cronbach alpha, inter-correlations

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Year: 2020 Vol.: 69 No.: 2

Record ID: 129 [ Page 1 of 4, No. 32 ]

A Validation of the Non-Parametric Continuous Norming Procedure

Authors: Melissa Jane Siy and Francisco N. de los Reyes

Abstract:

This study analyzed and validated the statistical aspect of the nonparametric continuous norming technique, which is a method used in creating scores in psychometric tests. Using the Work Profile Questionnaire - Emotional Intelligence (WPQei) with Filipino sample respondents, the study was able to demonstrate how the norming technique can be used to create age-group-based scores (age norms). Based on the results, the models from the technique can produce useable scores in practice with acceptable adjusted R-squared values; however, some challenges emerged with regard to the process of choosing the smoothing parameter, consistency of the significant variables and coefficient signs of the model, and the calculation of the score tables. Bootstrapping is recommended in improving the robustness of the technique.

Keywords: norming, WPQei, age-group-based score tables, bootstrap

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Year: 2020 Vol.: 69 No.: 2

Record ID: 128 [ Page 1 of 4, No. 33 ]

A Sequential Markov Chain Model of FIFA World Cup Winners

Authors: Nehemiah A. Ikoba

Abstract:

In this paper, a sequential Markov chain conceptualization of the winners of the FIFA World Cup is presented. The aim was to capture the dynamics of the World Cup and predict the future winner via Markov chain analysis. A sequentially incremented state-space Markov chain is used to approximate the process of winning the FIFA World Cup. The corresponding Markov chains at every epoch where the state space increases were computed. The result of the analysis showed a close predictive ability of the model to predict the previous World Cup winners. It is predicted that a new winner may emerge in the 2022 World Cup in Qatar. However, if a new winner does not emerge, on the basis of both the sequential Markov chain and the first passage matrix of the conceptualized model, then Brazil is the most probable winner of the 2022 World Cup, followed by Italy and Germany. The sequential Markov chain approach can be applied to other sporting events and scenarios in which there is only a small probability that the number of observed states may increase from a small set of states.

Keywords: stochastic processes, sports analytics; transition probability matrix, mean first passage time, mean recurrence time

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Year: 2020 Vol.: 69 No.: 2

Record ID: 127 [ Page 1 of 4, No. 34 ]

Recursive Quantile Estimation through a Stochastic Algorithm

Authors: A. Bachir and K. Djeddour-Djaballah

Abstract:

In this paper, we propose an estimate of a quantile of an unknown population. By considering this problem as a stochastic approximation problem, we obtain an estimator of the quantile and provide the almost-sure convergence as well as the asymptotic normality of this estimator. Some simulation results are presented to show that the proposed estimator works well.

Keywords: stochastic approximation, non-parametric estimation, quantile estimation

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Year: 2020 Vol.: 69 No.: 1

Record ID: 126 [ Page 1 of 4, No. 35 ]

Penalty Analysis with Resampling Method for Sensory Evaluation

Authors: Reanne Len C. Arlan, James Roldan S. Reyes, and Mary Denelle C. Mariano

Abstract:

Penalty analysis is the most widely accepted approach in consumer oriented testing for sensory evaluation dealing with just about right (JAR) data. However, performing test of significance of penalizing sensory attribute on the overall acceptability of a product is limited due to the absence of mean dropâ€™s standard error. To address this limitation, this paper applied alternative approaches using bootstrap and jackknife resampling. Using sensory evaluation data of the soft serve vanilla-based ice cream of a certain fast food company, the ordinary and the proposed jackknifing penalty analyses yielded similar results. While both analyses found that â€œtoo much sweetnessâ€ as the most troublesome attribute, bootstrapping penalty analysis showed having a â€œtoo weak vanilla flavorâ€ has an effect of 0.45 mean drop point from the productâ€™s overall acceptability. Through empirical validation, jackknife mean drop estimates were found to be closer to the original mean drops and have smaller estimates of standard error as compared with the bootstrap mean drop estimates. Overall, bootstrapping penalty analysis was more conservative in determining critical sensory attributes. Moreover, the proposed jackknifing penalty analysis was able to depict similar results of the ordinary penalty analysis with further ability to calibrate problematic sensory attributes based on statistical evidence. This can offer a more powerful and useful evaluation tool for product development in the food industry.

Keywords: bootstrapping, jackknifing, just about right scale, 9-point hedonic score, mean drop, product development

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Year: 2020 Vol.: 69 No.: 1

Record ID: 125 [ Page 1 of 4, No. 36 ]

Evaluation of Sampling Methods for Content Analysis of Facebook Data

Authors: Xavier Javines Bilon and Jose Antonio R. Clemente

Abstract:

A methodological challenge for researchers performing content analysis on social media data involves deciding on a sampling procedure for obtaining content to be analyzed with least sampling error. The study used and recommended two different kinds of elementary unitâ€”post and dayâ€”that allow probability sampling of Facebook data, regardless of whether the sampling frame of all posts within the time period of interest is obtainable. Four sampling designs for post as elementary unit and five for day as elementary unitâ€”including three commonly used sampling options for content analysis: simple random sampling without replacement (SRSWOR), constructed week sampling, and consecutive day samplingâ€” were employed on Facebook data mined from Mocha Uson Blog from 2010 to 2018. Estimates for parameters, such as measures of user engagement and proportions of topic-related posts, were obtained at increasing sample sizes. Sampling designs for each elementary unit were evaluated by comparing the normalized area under the coefficient of variation curve (NAUCV) over the different sample sizes. For post as elementary unit, with content type as the stratification variable, stratified random sampling (StRS) using Neyman allocation based on total user engagement is recommended (average NAUCV = 31.28%). For day as elementary unit, SRSWOR is recommended (average NAUCV = 42.31%).

Keywords: content analysis, sampling method, social media analytics

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Year: 2020 Vol.: 69 No.: 1

Record ID: 124 [ Page 1 of 4, No. 37 ]

Bounds Testing Approach in Determining the Impact of Climate Change Indicators to the Rice Yield of Central Luzon

Authors: Hernan G. Pantolla and Rechel G. Arcilla

Abstract:

Crop production, among many others, is threatened by climate change. In the Philippines, one such crop is rice. Various models have been applied to investigate factors that affect the production of this grain. Specialized software has been utilized that simulates its production under different conditions. But these were done on a larger scale. Local and international organizations call for the creation of crop production models that are specific to locations which can describe their long- and short-term responses to the changes in the environment. Hence, a model is proposed that made use of the Bounds Testing feature of the Autoregressive Distributed Lag Model (ARDL) to determine the long-run and short-run effects of selected climate change indicators to the rice yield of Central Luzon, the Rice Granary of the Philippines. The findings showed that the speed of adjustment towards equilibrium is 1.26%. Precipitation, temperature, and atmospheric carbon dioxide concentration have long-run impacts, while the lagged differences of the yield itself and temperature as well as the first difference temperature have short-run effects. Sea surface temperature anomaly was found to have no significant contribution. The fitness of the generated ARDL Model was substantiated by several diagnostic tests performed. In addition, it showed gains in forecasting accuracy when compared with a baseline model. The results of this study could be used in decision-making and policy developments.

Keywords: autoregressive distributed lag, bounds testing, climate change indicators, rice yield, Central Luzon

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Year: 2020 Vol.: 69 No.: 1

Record ID: 123 [ Page 1 of 4, No. 38 ]

Analyzing the Impact of RPRH Law Implementation on Poverty Reduction in the Philippines

Authors: Michael Ralph M. Abrigo, Aniceto C. Orbeta Jr. and Alejandro N. Herrin

Abstract:

A key research question with relevance to policy is how the full implementation of the RPRH law, with attention to its three key elements, namely: comprehensive sexuality education, family planning, and maternal and child health, contributes to economic growth and poverty reduction. This study describes the various pathways in which these key elements would work their way to affecting fertility, human capital formation, and economic growth and poverty reduction. Among the pathways considered, recent studies have provided into the economic and social impact of preventing early childbearing, reducing maternal mortality, and preventing child stunting. However, a gap exists in measuring the effect of achieving couples desired fertility on economic growth and poverty reduction through its impact on the age structure, investments in human capital, and productivity. The study addresses this gap by estimating the economic gains from a full implementation of the RPRH law, with attention on the family planning component as opposed to delayed implementation or no implementation scenarios (â€œbusiness as usualâ€). The results show that helping couples achieve the desired number of children can potentially have substantial economic benefits in terms of more rapid economic growth arising from the first and second demographic dividends, which in turn accelerate poverty reduction both in terms of incidence and number of population.

Keywords: RPRH law, total fertility rate, demographic dividends, poverty reduction

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Year: 2020 Vol.: 69 No.: 1

Record ID: 122 [ Page 1 of 4, No. 39 ]

Consumer Expectations Survey and Quarterly Social Weather Survey: Evidence of Convergent Validity and Causality

Authors: Edsel L. Beja Jr.

Abstract:

The paper tests the convergent validity and causality of the Consumer Expectations Survey from the Bangko Sentral ng Pilipinas and the Quarterly Social Weather Survey from the Social Weather Stations. The results indicate that there is convergent validity; and that there is bi-direction causality. Further results reveal that both share a common set of determinants. Overall, the findings imply that the Consumer Expectations Survey and the Quarterly Social Weather Survey embody comparable information. As such, one can be a proxy measure of the other. For policy, the findings support the view that a monetary approach for controlling the overall performance of the country, especially with regard to the inflation rate, in conjunction with a fiscal approach for securing the provision of basic social services are key to an effective management of sentiments and for an improvement in the quality of life.

Keywords: Consumer Expectations Survey, Quarterly Social Weather Survey, convergent validity, cointegration, causality, Toda-Yamamoto procedure

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Year: 2019 Vol.: 68 No.: 2

Record ID: 121 [ Page 1 of 4, No. 40 ]

The Impact of Basic Education Reform on the Educational Participation of 16- to 17-year-old Youth in the Philippines

Authors: Geoffrey M. Ducanes and Dina Joan S. Ocampo

Abstract:

The study measures the impact on the school participation of 16 to 17-year-old learners in the Philippines of the implementation of the Senior High School program (SHS), which came into full effect in school year 2017â€“2018. The SHS program, which extended secondary education in the country from four to six years, was the most ambitious education reform action in the country in recent memory. The study found that the SHS program resulted in an increase in overall school participation rate of at least 13 percentage points among 16 to 17-year-olds. Perhaps more importantly, the increase in school participation rate was found to be highly progressive with those 16 to 17-year-olds in the two bottom income quintiles experiencing the highest increase in school participation rates by a wide margin. The study also found that both male and female students benefited from the program, although the gains appear to be higher for female students. Most of the gains in school participation were also found to occur outside Metro Manila.

Keywords: impact evaluation, logit regression, education reform, senior high school, gender in education

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Year: 2019 Vol.: 68 No.: 2

Record ID: 120 [ Page 1 of 4, No. 41 ]

Rapid Assessment of Real Estate Loan Disapproval via Predictive Modeling: A Case for the Philippines

Authors: Adrian Nicholas A. Corpuz and Joseph Ryan G. Lansangan

Abstract:

The Philippines is currently experiencing a housing backlog and is expected to reach 6.5 million by the year 2030 if nothing is done about this. It is in this context where the government and the private sector have partnered themselves to address the backlog. Financing institutions such as private banks and the Home Development Mutual Fund (i.e. Pag-IBIG) offer different home loans for Filipinos to be able to afford these houses. Using a local real estate developmentâ€™s dataset, the study explores the application of predictive models in quickly determining whether a client will likely be able to get a home loan approved or not once he or she submits the preliminary documents for a home loan. Results show that in terms of accuracy, decision trees and random forest are superior in predicting home loan disapproval than binary logistic regression. The best predictive model is the random forest model, and results show that the main determinants of getting a home loan approved are loan equity term, total contract price of the house, equity payment status, and the income of the client.

Keywords: real estate, home loan, binomial logistic regression, decision tree, CART, CTree, CHAID, random forest

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Year: 2019 Vol.: 68 No.: 2

Record ID: 119 [ Page 1 of 4, No. 42 ]

Spatio-temporal Analysis of Animal Rabies Cases in Negros Occidental, Philippines from 2012 to 2018

Authors: Joseph L. Arbizo, Philip Ian P. Padilla, Marilyn S. Sumayo, Mitzi N. Meracap, Andrea Marie N. Napulan, Rex Victor V. Dolorosa, Princess Monic Q. Velasco, Leslie S. Asorio, Thea Joy A. Clarito, James Matthew V. Recabar, Sael D. Rodriguez

Abstract:

Rabies is a dangerous and deadly zoonotic disease that infects domestic and wild animals and is transmissible to humans. Animal rabies, particularly of canine and feline type, is considered to be a serious threat to public health. Thus, all prevention and control efforts to reduce the cases of human rabies are stemming from the identification of high-risk communities where presence of canine or feline rabies cases are prevalent. Having recorded the highest number of cases in recent years, this research utilized the spatiotemporal analysis of animal rabies cases in Negros Occidental, Western Visayas, Philippines. The hotspot analysis was based on Getis-Ord-Gi* statistic to estimate statistically significant hotspots of animal rabies cases in the province. Mean center and standard deviational ellipse were performed to identify the epicenter, dispersion, and yearly directional trends of animal rabies cases. The emerging hotspot analysis based on the Getis-Ord-Gi* and Mann- Kendall statistics was performed to identify statistically significant clusters with significant temporal trend. Spatial analysis identified the major cities such as Bacolod City and Bago City and their surrounding cities and municipalities to be of high risk to animal rabies cases from 2012 to 2018. The epicenter of cases is slowly shifting from the northern part in earlier years towards the central part of the province in recent years. Twenty-six (26) space-time clusters of animal rabies cases in Negros Occidental were found to have â€œintensifyingâ€, â€œconsecutiveâ€, â€œoscillatingâ€, and â€œsporadicâ€ time trends. Two clusters classified as â€œnewâ€ hotspots were identified in the central part of the province. Results presented in this study could be of service for rabies cases surveillance, and in developing care and prevention programs for rabies control.

Keywords: animal rabies, spatio-temporal analysis, zoonosis, rhabodoviruses

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Year: 2019 Vol.: 68 No.: 2

Record ID: 118 [ Page 1 of 4, No. 43 ]

Influence of Physicochemical Water Parameters on the Total Weight of the Slipper-shaped Oyster Crassostrea iredalei in Visayas, Philippines

Authors: Michelle B. Besana, Ma. Ramela Angela C. Bermeo, and Philip Ian P. Padilla

Abstract:

Annual assessments of the total weights of the most abundant oyster species in the Philippines, Crassostrea iredalei, for two consecutive years were examined across ten different sampling sites in Visayas, Philippines. The ANCOVA model was used to investigate the effects of the different sampling sites and the different physicochemical water parameters as covariates on the log total weight. The ANOVA model was also used to examine site differences in the log total weight without taking into account the effects of the covariates. ANOVA and ANCOVA results were compared to distinguish site differences with and without the covariates in the model. The results from the ANOVA model revealed that there were significant differences in the mean log total weight between sites. In the final ANCOVA model, there were still significant differences in the mean log total weight between sites above and beyond the significant positive covariate effect of temperature. The observed variations in the total weight of oysters is most likely due to the varied underlying internal and external factors that affect oyster culture in their respective ecological habitat. The study also reflects both vulnerability and coping mechanism of the Philippine C. iredalei with the variations in temperature which are critical for developing tolerance for positive growth and survival. The findings of this study could promote patterns of selective breeding and culture practices with the additional consideration of environmental factors that would lead to a better understanding of the changing environmental conditions operating in the different culture sites that would help ensure better culture management and harvest.

Keywords: analysis of variance (ANOVA), analysis of covariance (ANCOVA), Tukey-Kramer method, physicochemical water parameter, slipper-shaped oyster Crassostrea iredalei

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Year: 2019 Vol.: 68 No.: 2

Record ID: 117 [ Page 1 of 4, No. 44 ]

Comparison of Official Data Sources and Construction of a Sampling Frame for Household-based Livestock Surveys in Nueva Ecija, Philippines

Authors: Anna Ma. Lourdes S. Latonio, Isidoro P. David, and Zita V.J. Albacea

Abstract:

A basic issue in designing a procedure on selecting a sample is how well the sampling frame corresponds to the target population from where the sample will come from, making the construction of the sampling frame a vital aspect in the development of a sampling design. The main purpose of this paper is to evaluate and compare different official data sources to be able to combine them into a sampling frame appropriate for use in household-based livestock operations surveys for major livestock types (carabao, cattle, goat, and swine) in the Province of Nueva Ecija. The data values of different official data sources available such as the Philippine Carabao Center Inventory (PCCI), Livestock and Poultry Survey (LPS), Barangay Agricultural Profiling Survey (BAPS), and Local Government Unit (LGU) records during 2007-2010 for the province were compared. Scatter plots, strengths of relationships and relative differences between observations on the same variables were analyzed. Specifically, relevant barangay level data common in the different surveys were used to perform the comparisons. Based on the assessment of the different data sources, a barangay level Household Based Livestock Frame (HBLF) was constructed. The constructed HBLF consists of 849 barangays as basic sampling units, each with attached livestock related information, and auxiliary information such as total rice area (TRA) and number of rice growers (NRG). The constructed HBLF can be used in the development of separate or combined sampling designs for household-based livestock surveys in the Province of Nueva Ecija.

Keywords: Agriculture, livestock, sampling, sampling frame, livestock inventory

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Year: 2019 Vol.: 68 No.: 2

Record ID: 116 [ Page 1 of 4, No. 45 ]

Sampling with Probability Proportional to Aggregate Size in Heterogeneous Populations: A Study of Design and Efficiency

Authors: Daniel David M. Pamplona

Abstract:

Sampling with probability proportional to aggregate size (PPAS) is compared with traditional design-unbiased sampling methods under different simulated population scenarios in the estimation of the population total. The study considered both accuracy and precision of the estimates in the comparison. Heterogeneous populations were simulated by exploring varying behaviors of an auxiliary variable and its relationship with the target variable. Results show that the optimality of estimates using PPAS sampling improve as the association between the target variable and auxiliary variable strengthens. Furthermore, PPAS sampling estimates are more stable under large variability in the population.

Keywords: Probability Proportional to Aggregate Size Sampling, Nonparametric Bootstrap, Simple Random Sampling, Probability Proportional to Size Systematic Sampling

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Year: 2019 Vol.: 68 No.: 2

Record ID: 115 [ Page 1 of 4, No. 46 ]

Nonparametric Test of Interaction Effect for 2²-Factorial Design with Unequal Replicates: Case of Poisson-Normal Multivariate Data

Authors: Mara Sherlin D. Talento, Marcus Jude P. San Pedro and Erniel B. Barrios

Abstract:

Multivariate Analysis of Variance (MANOVA) is fairly robust to the normality and constant variance assumptions provided that the data is generated from a balanced design. Issues with hypothesistesting arises when error-distribution is non-normal or when the data is generated from an unbalanced design. We propose a nonparametric method of testing interaction effect of the twofactor factorial design with multivariate response and possibly highly unbalanced replicates. Simulation studies indicated that the test is correctly-sized and increasing power with increasing effect-size, and increasing sample size. The parametric test based on MANOVA is incorrectl

Keywords: two-factor factorial design, unbalanced replicates, nonparametric, multivariate data, Poisson and Normal data

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Year: 2019 Vol.: 68 No.: 2

Record ID: 109 [ Page 1 of 4, No. 47 ]

Life in the Fast Food Lane: Understanding the Factors Affecting Fast Food Consumption among Students in the Philippines

Authors: Adina Faye Bondoc, Hannah Felise Florendo, Emilio Jefe Taguiwalo and John Eustaquio

Abstract:

The fast food industry in the Philippines is growing rapidly and is dominating the food service establishments. Together with this influx in fast food establishments is the increase in fast food consumption and an emergence of an unhealthy lifestyle and increase in obesity prevalence, not only among Filipinos, but around the world. The growth of the fast food industry has been aggressive, especially with its advertisements which have been known to target families and the youth. Previous studies have shown that the youth tend to be more affected by fast food obesity than adults. With this, the researchers decided to create a model for whether students eat at fast food chains using the 2011 Global school-based Student Health Survey in the Philippines. Before modelling, factor analysis was performed to bracket variables together. A total of 6 factors arose--namely vices, assistance from others, injuries and bullying, hygiene, active lifestyle, and diet. In modelling using the original variables, various methods were used for variable selection to reduce the forty-seven variables to a manageable number of predictors. These methods were the independent Chi-squared tests, Fisher Exact Tests, Forward and Backward Selection, and Analysis of Deviance. The resulting model showed that some of the most significant predictors for whether or not a student eats fast food is their frequency of drinking soft drinks, eating fruits, and feeling hungry due to lack of food in the house. The weight and sex of a student also significantly affected the response, in which the odds of eating at a fast food chain were for men were 33.84% lower than that of women, and a kilogram increase in a studentâ€™s weight increased their odds by 2.5%.

Keywords: fast food, nutrition, factor analysis, logistic regression, Poisson loglinear model, negative binomial loglinear model, Global School-Based Student Health Survey

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Year: 2019 Vol.: 68 No.: 1

Record ID: 108 [ Page 1 of 4, No. 48 ]

Exploring the Disparities on the Actualization of the Ideal Number of Children among Filipino Women

Authors: Patrisha Brynne Agbayani, Kimberly Baltazar, Excel Franco and John Eustaquio

Abstract:

Family size has been consistently associated with poverty incidence as shown by household survey data over time. According to the 2010 Census of Population, the average household size stands at 4.6 members. With this current situation, this research delves deeper into this vital familial attribute by determining the factors that influence the levels of disparity between a womanâ€™s actual and ideal number of children. Furthermore, the research aims to understand how the odds of actualization of the Filipinaâ€™s ideal number of children increases or decreases in relation to the factors that were considered in the study.

The regression model for multinomial response utilized in the study is the proportional-odds cumulative logistic regression model. The results of the study have shown that religious affiliation to the Roman Catholic church doubles the estimated odds of exceeding the ideal number of children among women. Meanwhile, the odds of exceeding the desired number of children decrease as financial status improves. Husband-related factors also affect the actualization of the ideal number of children. More importanly, the discrepancy on the woman and husbandâ€™s ideal number of children, as well as the experience of emotional abuse from spouse both leads to an increase in the estimated odds of exceeding desired fertility. Lastly, a womanâ€™s use of contraceptives to delay or avoid pregnancy and whether the woman wanted her last pregnancy are also significant factors that affect the actualization of a womanâ€™s ideal number of children.

Keywords: fertility preference, contraceptive behavior, cumulative logit model, multiple response models

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Year: 2019 Vol.: 68 No.: 1

Record ID: 107 [ Page 1 of 4, No. 49 ]

Optimal Variable Subset Selection Problem in Regression Analysis is NP-Complete

Authors: Paolo Victor T. Redondo

Abstract:

Combinatorial and optimization problems are classified into different complexity classes, e.g., whether an algorithm that efficiently solve the problem exists or a hypothesized solution to the problem can be quickly verified. The optimal selection of subset variables in regression analysis is shown to belong to a complexity class called NP-hard (Welch, 1982) in which solutions to the problems in the same class may not be easily (in terms of computing speed) proven optimal. Variable selection in regression analysis based on correlations is shown to be NP-hard, i.e., a complexity class of problems with easily verifiable solutions.

Keywords: optimal variable selection, regression analysis, np-completeness

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Year: 2019 Vol.: 68 No.: 1

Record ID: 106 [ Page 1 of 4, No. 50 ]

Computing the Combined Effect of Measurement Errors and Non-Response using Factor Chain-type Class of Estimator

Authors: Gajendra K. Vishwakarma, Neha Singh and Amod Kumar

Abstract:

In this article, we have suggested an efficient factor chain-type class of estimators in the presence of measurement error and nonresponse simultaneously. It is shown that several estimators can be generated from our proposed class of estimators. Mean Square Error of the proposed class of estimator are derived and compared with other existing estimators. The conditions under which proposed estimator is more efficient are obtained. A theoretical and empirical study has done to demonstrate the efficiency of this estimator over other existing estimators.

Keywords: Auxiliary variable, bias, mean square error, measurement errors, nonresponse, study variable

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Year: 2019 Vol.: 68 No.: 1

Modelling Portfolio Risk and Diversification Effects of a Portfolio Using the Exponential Distribution – Bivariate Archimedean Gumbel Copula Model

Local Quadratic Regression: Maximizing Performance via a Modified PRESS** for Bandwidths Selection

Spatiotemporal Patterns of COVID-19 Cases in Quezon City, Philippines

Utilization of Machine Learning, Government-Based and Non-Conventional Indicators for Property Value Prediction in the Philippines

Verification of Coffee Product Form and Determination of Conversion Rate From Coffee Dried Berries to Green Coffee Beans (GCB)

Determination of Dry Rubber Content of Rubber Cup Lump

Topic Identification and Classification of GooglePlay Store Reviews

A Bayesian Hierarchical Model for COVID-19 Cases in Mindanao Philippines

Hierarchical Bayesian Model for Correcting Reporting Delays in Dengue Counts

Estimating the Magnitude of the Poor Households in Metro Manila Using the Poisson Regression Model

Application of Consecutive Sampling Technique in a Clinical Survey for an Ordered Population: Does it Generate Accurate Statistics?

On Some Efficient Classes of Estimators Based on Higher Order Moments of an Auxiliary Attribute

An Application of CATANOVA and Logistic Regression on the Most Prevalent Sexually Transmitted Infection (A Case Study of the University of Nigeria Teaching Hospital)

Analytic Hierarchy Process with Rasch Measurement in the Construction of a Composite Metric of Student Online Learning Readiness Scale

Implementing an Effective Survey Operations for a Research and Development Survey in the Philippines

Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm

Two New Tests for Tail Independence in Extreme Value Models

Time Series Prediction of CO2 Emissions in Saudi Arabia Using ARIMA, GM(1,1), and NGBM(1,1) Models

Classes of Estimators under New Calibration Schemes using Non-conventional Measures of Dispersion

A New Compound Probability Model Applicable to Count Data

A Modified Ridge Estimator for the Logistic Regression Model

Modelling the Right-Tail Conditional Expectation and Variance of Various Philippine Stocks Return using the Class of Beta Generalized Pareto Distribution

Development of an Alternative Municipal and City Level Competitiveness Index in the Philippines

Time Series Approach for Modelling the Merger and Acquisition Series: An Application to Indian Banking System

Application of U-statistics in Estimation of Scale Parameter of Bilal Distribution

Analysis of Randomized Clinical Trial in the Presence of Non-Compliance: Comparison of Causal Models

An Improved Class of Estimators of Population Mean under Simple Random Sampling

A Procedure for the Generation of Small Area Estimates of Philippine Poverty Incidence

Using Uncertainty and Sobolâ€™ Sensitivity Analysis Techniques in the Evaluation of a Composite Provincial Level Food Security Index

Investigation of Factors Contributing to Indigenous Language Decline in Nigeria

Examining the Theoretical Assumption of a Six-fold Structure of Management Competency Sub-scales (MCS)

A Validation of the Non-Parametric Continuous Norming Procedure

A Sequential Markov Chain Model of FIFA World Cup Winners

Recursive Quantile Estimation through a Stochastic Algorithm

Penalty Analysis with Resampling Method for Sensory Evaluation

Evaluation of Sampling Methods for Content Analysis of Facebook Data

Bounds Testing Approach in Determining the Impact of Climate Change Indicators to the Rice Yield of Central Luzon

Analyzing the Impact of RPRH Law Implementation on Poverty Reduction in the Philippines

Consumer Expectations Survey and Quarterly Social Weather Survey: Evidence of Convergent Validity and Causality

The Impact of Basic Education Reform on the Educational Participation of 16- to 17-year-old Youth in the Philippines

Rapid Assessment of Real Estate Loan Disapproval via Predictive Modeling: A Case for the Philippines

Spatio-temporal Analysis of Animal Rabies Cases in Negros Occidental, Philippines from 2012 to 2018

Influence of Physicochemical Water Parameters on the Total Weight of the Slipper-shaped Oyster Crassostrea iredalei in Visayas, Philippines

Comparison of Official Data Sources and Construction of a Sampling Frame for Household-based Livestock Surveys in Nueva Ecija, Philippines

Sampling with Probability Proportional to Aggregate Size in Heterogeneous Populations: A Study of Design and Efficiency

Nonparametric Test of Interaction Effect for 22-Factorial Design with Unequal Replicates: Case of Poisson-Normal Multivariate Data

Life in the Fast Food Lane: Understanding the Factors Affecting Fast Food Consumption among Students in the Philippines

Exploring the Disparities on the Actualization of the Ideal Number of Children among Filipino Women

Optimal Variable Subset Selection Problem in Regression Analysis is NP-Complete

Computing the Combined Effect of Measurement Errors and Non-Response using Factor Chain-type Class of Estimator

Nonparametric Test of Interaction Effect for 2²-Factorial Design with Unequal Replicates: Case of Poisson-Normal Multivariate Data