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Panel VAR Models with Spatial Dependence Author info | Abstract | Publisher info | Download info | Related research | Statistics Jan Mutl () (University of Maryland College Park)
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I consider a panel vector autoregressive (panel VAR) model with cross sectional dependence of the model disturbances that can be characterized by a first order spatial autoregressive process. I derive asymptotic properties of a constrained maximum likelihood estimator that uses a consistent estimate of the degree of the spatial autocorrelation to concentrate the likelihood function. The asymptotic properties are derived taking the time dimension of the panel as fixed and letting the cross-sectional dimension tend to infinity.
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Paper provided by International Conferences on Panel Data in its series 10th International Conference on Panel Data, Berlin, July 5-6, 2002 with number
A5-2.
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Date of creation: Mar 2002Date of revision:
Handle: RePEc:cpd:pd2002:a5-2Contact details of provider:
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References listed on IDEAS 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.: O'Connell, Paul G. J., 1998.
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[Downloadable!] (restricted)
Kelejian, Harry H & Prucha, Ingmar R, 1998.
"A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances ,"
The Journal of Real Estate Finance and Economics ,
Springer, vol. 17(1), pages 99-121, July.
[Downloadable!] (restricted)
Kelejian, Harry H & Prucha, Ingmar R, 1999.
"A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model ,"
International Economic Review ,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
Arellano, Manuel & Bond, Stephen, 1991.
"Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations ,"
Review of Economic Studies ,
Blackwell Publishing, vol. 58(2), pages 277-97, April.
[Downloadable!] (restricted)
Michael Binder, Cheng Hsiao, and M. Hashem Pesaran, 2001.
"Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration ,"
Computing in Economics and Finance 2001
36, Society for Computational Economics.
[Downloadable!]
Other versions:
Michael Binder & Cheng Hsiao & M. Hashem Pesaran, 2000.
"Estimation and Inference In Short Panel Vector Autoregressions with Unit Roots And Cointegration ,"
CESifo Working Paper Series
CESifo Working Paper No. , CESifo Group Munich.
[Downloadable!] Binder, M. & Hsaio, C. & Pesaran, M.H., 2000.
"Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration ,"
Cambridge Working Papers in Economics
0003, Faculty of Economics, University of Cambridge.
[Downloadable!] Michael Binder & Cheng Hsiao & M. Hashem Pesaran, 2000.
"Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration ,"
Banco de España Working Papers
0005, Banco de España.
Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005.
"Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration ,"
Econometric Theory ,
Cambridge University Press, vol. 21(04), pages 795-837, August.
[Downloadable!] Hsaio, Cheng & Pesaran, M. Hashem & Tahmiscioglu, A. Kamil, 1998.
"Maximum Likelihood Estimation of Fixed Effects Dynamic Panel Data Models Covering Short Time Periods ,"
Cambridge Working Papers in Economics
9826, Faculty of Economics, University of Cambridge.
Other versions: Yoosoon Chang, 2000.
"Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency ,"
CIRJE F-Series
CIRJE-F-85, CIRJE, Faculty of Economics, University of Tokyo.
[Downloadable!]
Other versions: Michael Binder & Cheng Hsiao & Jan Mutl & M. Hashem Pesaran, 2002.
"Computational Issues in the Estimation of Higher-Order Panel Vector Autoregressions ,"
Computing in Economics and Finance 2002
345, Society for Computational Economics.
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