Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes
Yoon-Jin Lee (),
Ryo Okui () and
No 879, KIER Working Papers from Kyoto University, Institute of Economic Research
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.
Keywords: Autoregressive Sieve Estimation; Bias Correction; Double Asymptotics; Fixed Effects Estimator; GMM; Instrumental Variables Estimator. (search for similar items in EconPapers)
JEL-codes: C13 C23 C26 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:kyo:wpaper:879
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