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Pooled Mean Group Estimation of Dynamic Heterogeneous Panels

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Abstract

It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the Mean Group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this paper, we propose an intermediate procedure, referred to as the Pooled Mean Group (PMG) estimator, which constrains the long run coefficients to be identical, but allows the short run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: aggregate consumption functions for 24 OECD economies over the period 1962-93, and energy demand functions for 10 Asian developing economies over the period 1974-90.

Suggested Citation

  • Yongcheol Shin & Ron P Smith & Mohammad Hashem Pesaran, 1998. "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels," Edinburgh School of Economics Discussion Paper Series 16, Edinburgh School of Economics, University of Edinburgh.
  • Handle: RePEc:edn:esedps:16
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    More about this item

    Keywords

    heterogeneous dynamic panels; pooled mean group estimator; I(0) regressor; I(1) regressor; consumption function; energy demand;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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