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Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes


  • Yoon-Jin Lee

    () (Department of Economics, Indiana University)

  • Ryo Okui

    () (Institute of Economic Research, Kyoto University)

  • Mototsugu Shintani

    () (Department of Economics, Vanderbilt University)


In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly in nite order in the presence of individual effects. We utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality of the standard dynamic panel data estimators, including the xed effects estimator, the gen- eralized methods of moments estimator and Hayakawa's instrumental variables estimator, using double asymptotics under which both the cross-sectional sam- ple size and the length of time series tend to in nity. We also propose a bias- corrected xed effects estimator based on the asymptotic result. Monte Carlo simulations demonstrate that the estimators perform well and the asymptotic approximation is useful. As an illustration, proposed methods are applied to dynamic panel estimation of the law of one price deviations among US cities.

Suggested Citation

  • Yoon-Jin Lee & Ryo Okui & Mototsugu Shintani, 2013. "Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes," KIER Working Papers 879, Kyoto University, Institute of Economic Research.
  • Handle: RePEc:kyo:wpaper:879

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    Cited by:

    1. Ryo Okui & Takahide Yanagi, 2014. "Panel Data Analysis with Heterogeneous Dynamics," KIER Working Papers 906, Kyoto University, Institute of Economic Research.

    More about this item


    Autoregressive Sieve Estimation; Bias Correction; Double Asymptotics; Fixed Effects Estimator; GMM; Instrumental Variables Estimator.;

    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
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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