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Record ID: 70 [ Page 10 of 16, No. 1 ]
Nonparametric Bootstrap Test in a Multivariate Spatial-Temporal Model: A Simulation Study
Authors: Abubakar S. Asaad; Erniel B. Barrios
Abstract:
The assumptions of constant characteristics across spatial locations and constant characteristics across time points facilitates estimation in a multivariate spatial-temporal model. A test based on the nonparametric bootstrap in proposed to verify these assumptions. The simulation studies confirm that the proposed test procedures are powerful and correctly sized.
Keywords: coverage probability, robustness, spatial-temporal model
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Year: 2015 Vol.: 64 No.: 2
Record ID: 69 [ Page 10 of 16, No. 2 ]
Statistics for Applied Researchers: Bootstrap to the Rescue
Authors: Nabendu Pal; Suntaree Unhapipat
Abstract:
Availability of latest fast and affordable computing resources has empowered the statisticians tremendously. This has also given the applied researchers a unique edge to extend the frontier of their knowledge-base by taking advantage of sophisticated computational statistical tools where theoretical derivations of complex sampling distributions are often not required or can be bypassed. ‘Bootstrap method’ is one such tool which is being used widely in solving real-life problems that involve statistical inferences. This article is designed to present bootstrap in simple terms for the applied researchers with useful examples and show how it can go a long way in settling contentious issues with reasonably convincing results.
Keywords: Sampling distribution, p-value, nonparametric bootstrap, parametric bootstrap, test statistic.
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Year: 2015 Vol.: 64 No.: 1
Record ID: 68 [ Page 10 of 16, No. 3 ]
Developed Sampling Strategy in Evaluating Teaching Performance Through Student Ratings
Authors: James Roldan S. Reyes; Zita VJ. Albacea
Abstract:
This paper presents an alternative method apart from the current online or electronic approach, which is currently being used by some higher education institutions (HEIs), in administering student ratings for teachers. The developed method still employed the traditional paper approach but has been improved through the use of sampling application which includes sampling design, sample size, estimation technique, and strategic implementation. Three basic sampling designs such as simple random, stratified random, and cluster sampling were applied at three different sampling rates such as 25%, 50%, and 75%. For the empirical evaluation of the developed method, the Student Evaluation of Teachers (SET) of the University of the Philippines Los Baños (UPLB) was utilized using bootstrap resampling technique. Based on findings, stratified random sampling is the most appropriate sampling design to use with 50% of the students for each class section serving as SET evaluators. Results also revealed that bootstrap estimates of standard error are lower than that of the standard error using jackknife resampling procedure. Generally, the improved traditional paper approach same with the electronic approach could reduce the cost of administering student ratings. However, the electronic approach has a dilemma with regards to high non-response bias leading to invalid results. Thus, to minimize non-response error of the developed method, its standard protocol to administer the student ratings has been formulated.
Keywords: student ratings, traditional paper approach, sampling application, bootstrap resampling, jackknife resampling, non-response error
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Year: 2015 Vol.: 64 No.: 1
Record ID: 67 [ Page 10 of 16, No. 4 ]
Forecasting Time-Varying Correlation Using the DCC Model
Authors: John D. Eustaquio; Dennis S. Mapa; Miguel C. Mindanao; Nino I. Paz
Abstract:
Hedging strategies have become more and more complicated as assets being traded have become more interrelated to each other. Thus, the estimation of risks for optimal hedging does not involve only the quantification of individual volatilities but also include their pairwise correlations. Therefore a model to capture the dynamic relationships is necessary to estimate and forecast correlations of returns through time. Engle'ss dynamic conditional correlation (DCC) model is compared with other models of correlation. Performance of the correlation models are evaluated in this paper using only the daily log returns of the closing prices from January, 2000 to February, 2010 of the Peso-Dollar Exchange Rate and Philippine Stock Exchange index. Ultimately, Engle's DCC model is adopted because of its consistency with expectations. Though generally negative, correlation between these two returns is not really constant as the results indicated. The forecast evaluation of the models was divided into in-sample and out-of-sample forecast performance with short-term (i.e., 22-day, 60-day, and 125-day) and medium-term (250-day and 500-day) rolling window correlations, or realized correlations, as proxies for the actual correlation. Based on the root mean squared error and mean absolute error, the integrated DCC model showed optimal forecast performance for the in-sample correlation patterns while the mean-reverting DCC model had the most desirable forecast properties for dynamic long-run forecasts. Also, the Diebold-Mariano tests showed that the integrated DCC has greater predictive accuracy in terms of the 3-month realized correlations than the rest of the models.
Keywords: dynamic conditional correlation, Peso-Dollar exchange rate, PSE index, hedging
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Year: 2015 Vol.: 64 No.: 1
Record ID: 66 [ Page 10 of 16, No. 5 ]
Classification and Prediction of Suicidal Tendencies of the Youth in the Philippines: An Empirical Study
Authors: Stephen Jun V. Villejo
Abstract:
This paper investigates suicidal tendencies of youth in the Philippines based on the Young Adult Fertility and Sexuality Study (YAFS) 2002. The main goal of the paper is the classification and prediction of suicidal tendencies using classification algorithms. The different classification algorithms such as Classification and Regression Trees, random forests and conditional inference trees; and the logistic regression have consistent findings on the significant variables affecting suicidal tendencies. Due to the severely unbalanced classes of the response variable, the classification models have very poor predictive ability for the minority class although the over-all classification rate is high. A classification algorithm is proposed which improves the predictive ability in terms of balancing out the correct classification in the two classes of the response variable.
Keywords: classification, suicide, prediction, logistic regression
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Year: 2015 Vol.: 64 No.: 1
Record ID: 65 [ Page 10 of 16, No. 6 ]
Comparison of Tree-Based Methods in Identifying Factors Influencing Credit Card Ownership and Prediction Accuracy
Authors: Karl Anton M. Retumban
Abstract:
Factors influencing credit card ownership were identified using the data from Global Financial Inclusion Index Database of The World Bank and the tree-based methods: CART, boosting, and bagging. The prediction accuracy of the methods was compared in terms of the training and test error rate. Results on the world and Philippine data were compared. The factors influencing consumers to own a credit card are financial account ownership, highest educational attainment and age. This is the case both for the World and Philippine data. For the World data, the factors that influence credit card ownership are financial account ownership, debit card ownership, withdrawal frequency in personal account, highest educational attainment, current loan for home or apartment purchase, age, get cash in ATM and deposit cash in ATM. For the Philippine data, the influential factors to Filipino consumers are financial account ownership, age, income quintile, highest educational attainment, and deposit cash over the counter in branch of bank or financial institution. Among the procedures, boosting has the smallest test error rate while bagging has the largest training and test error rate, both for the world and Philippine data. CART and boosting has the smallest training error rate under the world data and Philippine data respectively.
Keywords: classification and regression trees, boosting, bagging, credit card ownership, Global Financial Inclusion Index Database
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Year: 2015 Vol.: 64 No.: 1
Record ID: 64 [ Page 10 of 16, No. 7 ]
Predicting Socioeconomic Classification in the Philippines: Beyond the Ordinal Logistic Regression Model
Authors: Michael Daniel C. Lucagbo
Abstract:
Socioeconomic classification (SEC) is an important construct to enable one to capture and understand changes in the structure of a society. The 1SEC 2012, a new scheme for identifying the SEC of Philippine households, predicts SEC using information on household characteristics through the ordinal logistic regression model. This study aims to improve the predictive ability of the 1SEC methodology by using state-of-the-art statistical learning techniques: discriminant analysis, support vector machines (SVM), and artificial neural networks (ANN), and thereby suggest a new scheme for predicting SEC. The results show that SVM and ANN exhibit improvements in exact-cluster prediction performance, suggesting alternative methods for predicting SEC.
Keywords: socioeconomic classification, ordinal logistic regression, discriminant analysis, support vector machines, artificial neural networks
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Year: 2015 Vol.: 64 No.: 1
Record ID: 63 [ Page 10 of 16, No. 8 ]
Determinants of income class in Philippine households: Evidence from the Family Income and Expenditure Survey 2009
Authors: Stephen Jun Villejo; Mark Tristan Enriquez; Michael Joseph Melendres; Dexter Eric Tan; Peter Julian Cayton
Abstract:
The government has instituted projects aimed at helping the poor, and has implemented mechanisms to make the services accessible to them. The wisdom of the projects of the government should not be defeated by misidentification of deserving households to enjoy those projects which could be remedied through proper and thorough assessment of their economic status.The study aims to provide a methodology and model for classifying households using demographic and household assets that may be used in identifying recipients of poverty-targeted projects. Cluster analysis was employed to identify household classification using income data from the Family Income and Expenditure Survey 2009. Five income clusters were identified. To study the relationship between the income classes and several predictors of income identified from previous researches, a family of logistic regression models have been utilized, culminating to the generalized logistic regression model. Nine significant predictors were included in the final reduced model. The model is assessed to have good fit via multiple Hosmer and Lemeshow tests. These variables were the following: location of the household whether in NCR or not, or in urban or rural area; education and employment status of the household head; number of cars, air-conditioners, and television sets; and the building type and household type. The sensitivity table suggests that the model is biased towards predicting the lower income classes. The research has identified a viable methodology for classification of income classes for households.
Keywords: mutlinomial logistic model, income determinants, clustering methods
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Year: 2014 Vol.: 63 No.: 2
Record ID: 62 [ Page 10 of 16, No. 9 ]
Determinants of regional minimum wages in the Philippines
Authors: Lisa Grace S. Bersales; Michael Daniel C. Lucagbo
Abstract:
In the Philippines, the National Wages and Productivity Commission (NWPC) formulates policies and guidelines that Tripartite Wage and Productivity Boards use in determining minimum wages in their respective regions. Reviews of the implementation of the minimum wage determination have been done in past studies to determine which of the factors listed by NWPC for consideration by the wage boards are actually used to determine minimum wage. Results indicated that the significant determinant of minimum wage is consumer price index. Two stage least squares estimation of a Fixed Effects Model for Panel Data for the period 1990-2012 showed that significant determinants of regional minimum wage for non-agriculture are: Consumer Price Index, Gross Regional Domestic Product, and April employment rate. The lower and upper estimates from the estimated equation of the Fixed Effects Model for Panel Data may provide intervals that the wage boards can use in making the final determination of minimum wage. The following shocks which would likely introduce abnormal wage setting behavior on the part of the wage boards were not significant: 1997-1998 - Asian Financial Crisis; 2002 - spillover effects from U.S. technology bubble burst; 2008-2009 - spillover effects from Global Financial Crisis.
Keywords: tripartite wage and productivity boards, minimum wage, fixed effects models for panel data, shocks, two stage least squares, fixed effects model for panel data
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Year: 2014 Vol.: 63 No.: 2
Record ID: 61 [ Page 10 of 16, No. 10 ]
The link between expenditure on contraceptives and number of young dependents in the Philippines
Authors: Michael Daniel C. Lucagbo; Genica Peye C. Alcaraz; Kristina Norma B. Cobrador; Elaine Japitana; Gelli Anne Q. Sadsad
Abstract:
The growing population of the Philippines hinders the country from achieving economic development due to the limited resources available. The 2010 Census on Population and Housing (CPH) reports that the Philippine population has struck 92.1 million, a 15.8-million increase from the 76.3 million population size reported in 2000. Moreover, the relationship between population and family size, on the one hand, and poverty incidence on the other, has been established through econometric models showing the causality between presence of young dependents in a household and household welfare. Using the Family Income and Expenditure Survey (FIES) 2009 data, this study examines the factors affecting the number of young dependents in a household, and focuses in particular on the household’s level of contraceptive expenditure. The negative binomial regression model is used to quantify the effect of the factors and predict the average number of young dependents in a household. This model allows for overdispersion in the data. Results show that for every P10,000 increase in total expenditure on contraceptives for a period of six months, the mean number of young dependents decreases by 3.7%. Other demographic variables such as education of household head and income of the household are controlled for in the study.
Keywords: Young dependents, contraceptive expenditure, negative binomial regression, overdispersion
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Year: 2014 Vol.: 63 No.: 2