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Pooling Under Misspecification: Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques

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  • Baltagi, Badi H.

Abstract

Two different methods for pooling time series of cross section data are used by economists. The first method, described by Kmenta, is based on the idea that pooled time series of cross sections are plagued with both heteroskedasticity and serial correlation.The second method, made popular by Balestra and Nerlove, is based on the error components procedure where the disturbance term is decomposed into a cross-section effect, a time-period effect, and a remainder.Although these two techniques can be easily implemented, they differ in the assumptions imposed on the disturbances and lead to different estimators of the regression coefficients. Not knowing what the true data generating process is, this article compares the performance of these two pooling techniques under two simple setting. The first is when the true disturbances have an error components structure and the second is where they are heteroskedastic and time-wise autocorrelated.First, the strengths and weaknesses of the two techniques are discussed. Next, the loss from applying the wrong estimator is evaluated by means of Monte Carlo experiments. Finally, a Bartletfs test for homoskedasticity and the generalized Durbin-Watson test for serial correlation are recommended for distinguishing between the two error structures underlying the two pooling techniques.

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  • Baltagi, Badi H., 1986. "Pooling Under Misspecification: Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques," Econometric Theory, Cambridge University Press, vol. 2(3), pages 429-440, December.
  • Handle: RePEc:cup:etheor:v:2:y:1986:i:03:p:429-440_01
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    Cited by:

    1. Giorgio Calzolari & Laura Magazzini, 2012. "Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood," Empirical Economics, Springer, vol. 43(1), pages 145-152, August.
    2. Juan L. Eugenio-Martin & Noelia Martín-Morales & M. Thea Sinclair, 2008. "The Role of Economic Development in Tourism Demand," Tourism Economics, , vol. 14(4), pages 673-690, December.
    3. Subramanian Rangan & Metin Sengul, 2009. "Information technology and transnational integration: Theory and evidence on the evolution of the modern multinational enterprise," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 40(9), pages 1496-1514, December.
    4. Sophia Delipalla & Owen O'Donnell, 1998. "The Comparison Between Ad Valorem and Specific Taxation under Imperfect Competition: Evidence from the European Cigarette Industry," Studies in Economics 9802, School of Economics, University of Kent.
    5. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    6. Dragan Miljkovic & Gary Brester & John Marsh, 2003. "Exchange rate pass-through, price discrimination, and US meat export prices," Applied Economics, Taylor & Francis Journals, vol. 35(6), pages 641-650.
    7. Kaushik Deb & Massimo Filippini, 2013. "Public Bus Transport Demand Elasticities in India," Journal of Transport Economics and Policy, University of Bath, vol. 47(3), pages 419-436, September.
    8. Chen, Kevin Z. & D. Meilke, Karl & Turvey, Calum, 1999. "Income risk and farm consumption behavior," Agricultural Economics, Blackwell, vol. 20(2), pages 173-183, March.
    9. Fotopoulos, Georgios & Spence, Nigel, 1999. "Net entry behaviour in Greek manufacturing: consumer, intermediate and capital goods industries," International Journal of Industrial Organization, Elsevier, vol. 17(8), pages 1219-1230, November.

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