Quarterly Journal of Econometrics Research

Published by: Conscientia Beam
Online ISSN: 2411-0523
Print ISSN: 2518-2536
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No. 1

Assessment of Mental Health of Undergraduate Students Based on Age: A Bayesian Ordinal Quantile Regression Approach

Pages: 12-17
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Assessment of Mental Health of Undergraduate Students Based on Age: A Bayesian Ordinal Quantile Regression Approach

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DOI: 10.18488/journal.88.2020.61.12.17

Nwakuya M. T.

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Nwakuya M. T. (2020). Assessment of Mental Health of Undergraduate Students Based on Age: A Bayesian Ordinal Quantile Regression Approach. Quarterly Journal of Econometrics Research, 6(1): 12-17. DOI: 10.18488/journal.88.2020.61.12.17
The traditional frequentist quantile regression makes minimal assumptions that accommodate errors that are not normal given that the response variable (y) is continuous even in Bayesian framework. However inference on these models where y is not continuous proves to be challenging particularly when the response variable is an ordinal data. This paper portrays the idea of Bayesian quantile estimation on ordinal data. This method utilizes the latent variable inferential framework. Estimation was done using Markov chain Monte Carlo simulation with Gibbs sampler where the cut points were set ahead of time and remained fixed all through the analysis. The method was applied in a mental health study of University undergraduate students. Investigations of the model exemplify the practical utility of Bayesian ordinal quantile models. In this paper we were able to investigate the mental health state of undergraduate students at different points in the distribution of their ages. Our findings show that the age of the students has a significant effect on their mental health. The results revealed that at 25th, 50th and 75th quantiles the ages had a negative effect on their mental health while at the 95th quantile the effect was positive. This study was able to show that older undergraduate students are more mentally equipped to withstand the stress of higher learning in the University.
Contribution/ Originality
The paper's primary contribution is to apply Bayesian ordinal quantile regression to mental health analysis. The study utilized the Gibbs sampler with fixed cut-points. It portrayed insight to the effect of age on the mental health of undergraduate students at different points on the age distribution.

Panel Data Estimators in the Presence of Serial and Spatial Correlation with Panel Heteroscedasticity: A Simulation Study

Pages: 1-11
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Panel Data Estimators in the Presence of Serial and Spatial Correlation with Panel Heteroscedasticity: A Simulation Study

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DOI: 10.18488/journal.88.2020.61.1.11

Nwakuya Maureen Tobechukwu , Ijomah Maxwell Azubuike

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Nwakuya Maureen Tobechukwu , Ijomah Maxwell Azubuike (2020). Panel Data Estimators in the Presence of Serial and Spatial Correlation with Panel Heteroscedasticity: A Simulation Study. Quarterly Journal of Econometrics Research, 6(1): 1-11. DOI: 10.18488/journal.88.2020.61.1.11
Panel data analysis is often faced with the issue of errors having arbitrary correlation across time for a particular individual “I” (serial correlation) and/or errors having arbitrary correlation across individuals at a moment in time (spatial correlation) with error disturbances having non constant variance. This study examined some panel data estimators in the presence of serial and spatial autocorrelation with panel heteroscedasticity. The study was done using two different sets of data simulated separately with ?=0.95 & 0.50. For each set of simulations short and long panels were considered for different sample sizes. The analysis considered two settings were rho is considered to be panel-specific (?i) and where rho is considered to be common for all panels (?). The estimators were examined based on bias, overconfidence and relative efficiency. The results produced evidence that the size of the autocorrelation coefficient ? affects the general performance of an estimator. Comparison of the estimators showed that Panel Corrected Standard Error Estimator (PCSE) produced better results than the other estimators considered in this work. But it was seen not to do very well in small samples and short panels. In terms of relative efficiency Park-Kmenta estimator was found to be more efficient that PCSE and PWLS (Panel Weighted Least Square Estimator). This paper has been able to show that the size of rho at the long run has an impact on the performance of the estimators, it showed that a small size of rho tends to increase overconfidence. The paper also revealed that Park-Kmenta estimator even with its flaws is still the most efficient estimator compared to the PCSE and PWLS. and it also substantiated the fact that PCSE performs badly in samples especially when N>T.
Contribution/ Originality
This study is one of the few studies which have investigated the effect of the size of autocorrelation coefficient on the estimators based on the flaws of Parks-Kmenta estimator, which has mislead most researchers into becoming more inclined to use PCSE without considering others credits of Parks-kmenta estimator.