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
Views 5410      Downloads 178
Authors
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
Article Tools
Follow on us
Abstract
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.
Keywords
Panel, Heteroscedasticity, Autocorrelation, Homogeneity
To cite this article
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. doi: 10.11648/j.ajtas.20150403.25
References
[1]
Cribari Neto, F. (2004), “Asymptotic panel inference under heteroscedasticity of unknown form,” Computational Statistics and Data Analysis, vol. 45, pp. 215–233., April 1955. (references)
[2]
Ferrari and Cordeiro (2000), ‘Improved heteroscedasticity consistent covariance matrix estimators’, Biometrika, vol. 87, pp 907-918
[3]
Grizzle, J. E. and D. M. Allen (1969), ‘Analysis of growth and dose response curves’, Biometrics Vol. 25, pp 357-381
[4]
Grunfeld, Y (1958), The Determinants of Corporate Investment, PhD thesis, University of Chicago.
[5]
Hausman, J. (1978), “Specification Tests in Econometrics”, Econometrica Vol. 46, pp 1251-1271
[6]
Kleiber and Zeileis (2010), ‘The Grunfeld data at 50’, German Economic Review.
[7]
Laird, Nan M. and James H. Ware (1982), ‘Random-effects models for longitudinal data’, Biometrics vol. 38, pp 963-974.
[8]
Lazarsfeld, Paul F. and Marjorie Fiske (1938), ‘The panel as a new tool for measuring opinion’, Public Opinion Quarterly 2 pp. 596–612
[9]
Long, J. S and L. Ervin (2000), ‘Using heteroscedasticity-consistent standard errors in the linear regression model’, The American Statistician Vol. 54, pp 217–224.
[10]
Marschak, J. (1939), “On combining market and budget data in demand studies”, Econometrica, Vol. 7, pp 332-335
[11]
Pesaran, M. (2004), ‘General diagnostic ttest for cross section dependence in panels’, CESinfo Working Papers Series p. 1229
[12]
Potthoff, R. F. and S. N. (1964) Roy (1964), ‘A generalized multivariate analysis of variance model useful especially for growth curve problems.’, Biometrika Vol. 51, 313–326
[13]
Rao, C. R. (1965), ‘The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves.’ Biometrika Vol. 52, 447–458.
[14]
Tobin, J. (1950), “A statistical demand function for food in the U.S.A”, Journal of the Royal Statistical Society, Series A, pp 113-141 Vol. 52, 447–458.
[15]
Wishart, J. (1938), ‘Growth-rate determinations in nutrition studies with the bacon pig, and their analysis.’ Biometrika Vol. 30, 16–28.
[16]
Wooldridge, J. (2002), ‘Econometric analysis of crosssection and panel data’, MIT press.
[17]
Zivot, E. and J. Wang (2003), Modeling Financial Time Series with S-plus, Springer-Verlag, New York.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186