Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 879.
Date of creation: Oct 2013
Date of revision:
Contact details of provider:
Postal: Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501
Web page: http://www.kier.kyoto-u.ac.jp/eng/index.html
More information through EDIRC
Autoregressive Sieve Estimation; Bias Correction; Double Asymptotics; Fixed Effects Estimator; GMM; Instrumental Variables Estimator.;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-10-18 (All new papers)
- NEP-ECM-2013-10-18 (Econometrics)
- NEP-ETS-2013-10-18 (Econometric Time Series)
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Akihisa Shibata).
If references are entirely missing, you can add them using this form.