Youssef, Ahmed H. and Abonazel, Mohamed R. (2009): A Comparative Study for Estimation Parameters in Panel Data Model. Published in: InterStat Journal , Vol. 2009, No. May, No. 2 (9 May 2009): pp. 117.

PDF
MPRA_paper_49713.pdf Download (715kB)  Preview 
Abstract
This paper examines the panel data models when the regression coefficients are fixed, random, and mixed, and proposed the different estimators for this model. We used the Mote Carlo simulation for making comparisons between the behavior of several estimation methods, such as Random Coefficient Regression (RCR), Classical Pooling (CP), and Mean Group (MG) estimators, in the three cases for regression coefficients. The Monte Carlo simulation results suggest that the RCR estimators perform well in small samples if the coefficients are random. While CP estimators perform well in the case of fixed model only. But the MG estimators perform well if the coefficients are random or fixed.
Item Type:  MPRA Paper 

Original Title:  A Comparative Study for Estimation Parameters in Panel Data Model 
English Title:  A Comparative Study for Estimation Parameters in Panel Data Model 
Language:  English 
Keywords:  Panel Data Model, Random Coefficient Regression Model, Mixed RCR Model, Monte Carlo Simulation, Pooling Cross Section and Time Series Data, Mean Group Estimators, Classical Pooling Estimators. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C33  Panel Data Models ; Spatiotemporal Models C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling 
Item ID:  49713 
Depositing User:  Dr. Mohamed R. Abonazel 
Date Deposited:  10 Sep 2013 11:13 
Last Modified:  03 Oct 2019 19:21 
References:  1. Baltagi, B. (2008), Econometric Analysis of Panel Data. 4th ed., John Wiley and Sons Ltd. 2. Carlson, R. (1978), “Seemingly Unrelated Regression and the Demand for Automobiles of Different Sizes.”, Journal of Business, Vol. 51, pp. 243262. 3. Dielman, T. E. (1989), Pooled CrossSectional and Time Series Data Analysis. New York: Marcel Dekker. 4. Gendreau, B. and Humphrey, D. (1980), “Feedback Effects in the Market Regulation of Bank Leverage: A TimeSeries and CrossSection Analysis.”, Review of economic Statistics, Vol. 62, pp. 276280. 5. Hsiao, C. (1985), “Benefits and Limitations of Panel Data.”, Econometric Review, Vol. 4, pp. 121174. 6. Hsiao, C. (2003), Analysis of Panel Data. 2th ed., Cambridge: Cambridge University Press. 7. Hsiao, C. and Pesaran, M. H. (2004), “Random Coefficient Panel Data Models.”, IEPR Working Paper 04.2, University of Southern California. 8. Murtazashvili, I. and Wooldridge, J. M. (2008), “Fixed Effects Instrumental Variables Estimation in Correlated Random Coefficient Panel Data Models.”, Journal of Econometrics, Vol. 142, pp. 539552. 9. Pesaran, M.H. and R. Smith (1995), “Estimation of LongRun Relationships from Dynamic Heterogeneous Panels.”, Journal of Econometrics, Vol. 68, pp. 79114. 10. Rao, C. R. and Mitra, S. (1971), Generalized Inverse of Matrices and Its Applications. John Wiley and Sons Ltd. 11. Swamy, P. (1970), “Efficient Inference in a Random Coefficient Regression Model.”,Econometrica, Vol. 38, pp. 311323. 12. Swamy, P. (1971), Statistical Inference in Random Coefficient Regression Models. New York: SpringerVerlag. 13. Swamy, P. (1973), “Criteria, Constraints, and Multicollinearity in Random Coefficient Regression Model.”, Annals of Economic and Social Measurement, Vol. 2, pp. 429450. 14. Swamy, P. (1974), Linear Models with Random Coefficients. in Frontiers in Econometrics (Ed. P. Zarembka).”, New York: Academic Press, Inc., pp. 143168. 15. Zellner, A. (1962), “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests of Aggregation Bias.”, J.A.S.A., Vol. 57, pp. 348368. 16. Zellner, A. (1963), “Estimators for Seemingly Unrelated Regressions Equations: Some Exact Finite Sample Results.”, J.A.S.A., Vol. 58, pp. 977992. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/49713 