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

Listed author(s):
  • In Choi

    ()

    (School of Economics, Sogang University, Seoul)

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    This paper proposes new estimators for the panel autoregressive (PAR) models with short time dimensions (T) and large cross sections (N). These estimators are based on the cross-sectional regression model using the first time series observations as a regressor and the last as a dependent variable. The regressors and errors of this regression model are correlated. The first estimator is the maximum likelihood estimator (MLE) under the assumption of normal distributions. This estimator is called the cross-sectional MLE (CSMLE). The second estimator is the bias-corrected pooled least squares estimator (BCPLSE) that eliminates the asymptotic bias of PLSE by using the CSMLE. The CSMLE and BCPLSE are extended to the PAR model with endogenous time-variant and time-invariant regressors. The CSMLE and BCPLSE provide consistent estimates of the PAR coefficients for stationary, unit root and explosive PAR models, estimate the coefficients of time-invariant regressors consistently and can be computed as long as T>=2. Their finite sample properties are compared with those of some other estimators for the PAR model of order 1. The estimators of this paper are shown to perform quite well in finite samples.

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    File URL: ftp://163.239.156.99/wpaper/CI_RIME_2016_10.pdf
    File Function: First version, 2016
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    Paper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 1610.

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    Length: 40 pages
    Date of creation: Jun 2016
    Handle: RePEc:sgo:wpaper:1610
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    1. Hahn, Jinyong & Hausman, Jerry & Kuersteiner, Guido, 2007. "Long difference instrumental variables estimation for dynamic panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 574-617, October.
    2. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    3. Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
    4. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    5. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    6. Hahn, Jinyong, 1999. "How informative is the initial condition in the dynamic panel model with fixed effects?," Journal of Econometrics, Elsevier, vol. 93(2), pages 309-326, December.
    7. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    8. Phillips, Peter C.B. & Han, Chirok, 2008. "Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root," Econometric Theory, Cambridge University Press, vol. 24(03), pages 631-650, June.
    9. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
    10. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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