Short guides to static panel data regression model estimator
List of Authors
  • Hasni Abdullah , Zahariah Sahudin

Keyword
  • Panel Data, Pooled Ordinary Least Square, Fixed Effect, Random Effect

Abstract
  • This paper elaborates on the use of three regression model estimators under the classification of static panel data. In order to select the best regression model estimator for panel data analysis, the researcher should focus on conducting several tests to avoid bias in the estimation results. The Breusch Pagan Lagrangian Multiplier test (Breusch Pagan test) is a simple test carried out in the panel data analysis to check for heteroscedastic disturbances in the linear regression model in making the decision to choose whether Pooled Ordinary Least Square or Random Effects. If the Breusch Pagan test shows the rejection of the null hypothesis, it indicates that the Random Effect model is more appropriate than Pooled Ordinary Least Square because the data have a panel effect (unobserved heterogeneity). The panel effect should be checked using the Hausman test in order to identify whether the effects are correlated or uncorrelated with the regressors. In the case of rejection of the null hypothesis, the Fixed Effect estimator is a more appropriate or unbiased estimator to analyze the data.

Reference
  • 1. Baltagi, B. H. (2013). Econometric analysis of panel data.5rd Edition. John Wiley: Europe.

    2. Bruderl, J. (2005). Panel data analysis. University of Mannheim, March.

    3. Davidson, R., & MacKinnon, J. G. (2008). Bootstrap inference in a linear equation estimated by instrumental variables. Econometric Journals, 11, 443-477.

    4. Faustino, H. C., & Leitao, N. C. (2007). Intra-industry trade: A static and dynamic panel data analysis. International Advances in Economic Research, 13, 313-333.

    5. Greene, W. H. (2008). Econometric Analysis. Upper Saddle River, NJ: Pearson Prentice Hall.

    6. Gujarati, D. N. (2003). Basic Econometrics, International Edition - 4th Edition: McGraw-Hill Higher Education.

    7. Gujarati, D. N. & Porter, D. C. (2009). Basic Econometrics, International Edition - 5th Edition.: McGraw-Hill Higher Education.

    8. Hsiao, C. (2014). Analysis of panel data. 3rd Edition. Cambridge University Press: New York.

    9. Hsiao, C. (2006). Panel data analysis - Advantages and challenges. Institute for Economic Policy Research(IEPR) Working Paper, 6(49).

    10. Nwakuya, M. T, & Ijomah, M. A. (2017). Fixed effect versus random effects modelling in a panel data analysis: A consideration of economic and political indicators in six African countries. International Journal of Statistics and Applications, 7(6), 275-279.

    11. Park, H. M. (2011). Practical guides to panel data modelling: A step-by-step analysis using stata. (Tutorial Working Paper). Graduate School of International Relations, International University of Japan.

    12. Parlow, A. (2010). Panel data analysis in Stata. UMW Economics Department.

    13. Song, H., & Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern economic approaches. Pergamon: Cambridge.