The Hausman-Taylor Panel Data Model with Serial Correlation
AbstractThis paper modifies the Hausman and Taylor (1981) panel data estimator to allow for serial correlation in the remainder disturbances. It demonstrates the gains in efficiency of this estimator versus the standard panel data estimators that ignore serial correlation using Monte Carlo experiments.
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Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 136.
Length: 8 pages
Date of creation: Mar 2012
Date of revision:
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More information through EDIRC
Panel Data; Fixed Effects; Random Effects; Instrumental Variables; Serial Correlation;
Other versions of this item:
- Baltagi, Badi H. & Liu, Long, 2012. "The Hausman–Taylor panel data model with serial correlation," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1401-1406.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-07 (All new papers)
- NEP-ECM-2013-01-07 (Econometrics)
- NEP-ETS-2013-01-07 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University.
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