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On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models

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  • Kazuhiko Hayakawa

Abstract

In this paper, we consider dynamic panel data models with possibly nonstationary initial conditions. We derive the asymptotic properties of the GMM estimators with various kinds of instruments when both N and T are large, where N and T denote the dimensions of the cross section and time series. We find that when initial conditions are nonstationary and the degree of heterogeneity, which is measured by the variance ratio of individual effects to the disturbances, is large, the biases and variances of the GMM estimators become small. We demonstrate that this is because the correlation between the lagged dependent variable and instruments gets larger due to the unremoved individual effects. This implies that the instruments become strong when initial conditions are nonstationary and the degree of heterogeneity is large. For the purpose of comparison, we also derive the asymptotic properties of the within groups and the LIML estimators. Numerical studies are conducted to assess the properties of these estimators.

Suggested Citation

  • Kazuhiko Hayakawa, 2008. "On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d07-245, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hst:hstdps:d07-245
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    4. Hayakawa, Kazuhiko, 2009. "On the effect of mean-nonstationarity in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 153(2), pages 133-135, December.

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    More about this item

    Keywords

    Dynamic panel data models; many instruments; generalized method of moments estimator; nonstationary initial conditions; degree of heterogeneity.;
    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

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