Modeling Panel Data: Comparison of GLS Estimation and Robust Covariance Matrix Estimation
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 3, May 2015, Pages: 185-191
Received: May 8, 2015;
Accepted: May 17, 2015;
Published: May 28, 2015
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Victor Muthama Musau, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony Gichuhi Waititu, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony Kibira Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
The proliferation of panel studies which has been greatly motivated by the availability of data and greater capacity for modeling the complexity of human behavior than a single cross-section or time series data has led to the rise of challenging methodologies for estimating the data sets. Much controversy on these methodologies is the under-estimation of the standard errors leading to wrong conclusions of the involved hypothesis test as well as making inappropriate inference to the underlying model parameters. This is due to the heteroscedasticity and autocorrelation nature of the disturbance term in the classical linear regression model. This study sought to estimate linear-panel model parameters using conventional regression techniques which have the capacity to address the correlation and heteroscedasticity problem. By relaxing the homogeneity and non-correlation properties of the disturbance term in the classical linear regression model, we employed the generalized least squares method to estimate the model parameters. Using the available White Heteroscedasticity Consistent estimators i.e. HC0, HC1, HC2, HC3 and HC4, we also obtained estimates for the generalized ordinary least squares covariance matrix.
Victor Muthama Musau,
Anthony Gichuhi Waititu,
Anthony Kibira Wanjoya,
Modeling Panel Data: Comparison of GLS Estimation and Robust Covariance Matrix Estimation, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 3,
2015, pp. 185-191.
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