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Initial conditions of dynamic panel data models: on within and between equations

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  • Lung-fei Lee
  • Jihai Yu

Abstract

SummaryThis paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly defined, we show that the random effects model can be decomposed into a within equation and a between equation; hence, the random effects estimate is a pooling of the within and between estimates. Thus, our paper extends the pooling in the static panel data model (Maddala, 1971a) to the setting of dynamic panel data. This decomposition of a dynamic panel data model is revealing and valuable for estimation and the formulation of a Hausman test to test the possible correlation of individual effects with included regressors. Monte Carlo experiments are conducted to investigate the finite sample performance of estimators and the Hausman test. An empirical application of growth convergence in OECD countries is provided.

Suggested Citation

  • Lung-fei Lee & Jihai Yu, 2020. "Initial conditions of dynamic panel data models: on within and between equations," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 115-136.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:1:p:115-136.
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    File URL: http://hdl.handle.net/10.1093/ectj/utz015
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    Cited by:

    1. Anna Gloria Billé & Marco Rogna, 2022. "The effect of weather conditions on fertilizer applications: A spatial dynamic panel data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 3-36, January.
    2. Sung, Bongsuk & Soh, Jin Young & Park, Chun Gun, 2022. "Comparing government support, firm heterogeneity, and inter-firm spillovers for productivity enhancement: Evidence from the Korean solar energy technology industry," Energy, Elsevier, vol. 246(C).
    3. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.

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