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Cross-sectional quasi maximum likelihood and bias-corrected pooled least squares estimators for short dynamic panels

In Choi () and Sanghyun Jung ()
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Sanghyun Jung: Department of Economics, Sogang University, Seoul

No 2007, Working Papers from Research Institute for Market Economy, Sogang University

Abstract: This paper proposes new estimators for the panel autoregressive (PAR) model of order 1 with short time dimensions and large cross sections. These estimators are based on the cross-sectional regression model using the rst time series ob- servations as a regressor and the last as a dependent variable. The regressors and errors of this regression model are correlated. The rst estimator is the quasi maximum likelihood estimator (QMLE). The second estimator is the bias- corrected pooled least squares estimator (BCPLSE) that eliminates the asymp- totic bias of the pooled least squares estimator by using the QMLE. The QMLE and BCPLSE are extended to the PAR model with endogenous regressors. The QMLE and BCPLSE provide consistent estimates of the PAR coe¢ cients for stationary, unit root and explosive PAR models and consistently estimate the coe¢ cients of endogenous regressors. Their nite sample properties are com- pared with those of some other estimators for the PAR model of order 1. This papers estimators are shown to perform quite well in nite samples.

Keywords: dynamic panels; quasi maximum likelihood estimator; pooled least squares estimator; stationarity; unit root; explosive root (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020
New Economics Papers: this item is included in nep-ecm and nep-ore
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ftp://163.239.156.99/wpaper/CI_RIME_2020_07.pdf First version, 2020 (application/pdf)

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Journal Article: Cross-sectional quasi-maximum likelihood and bias-corrected pooled least squares estimators for short dynamic panels (2021) Downloads
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