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Higher-Order Asymptotic Expansions of the Least-Squares Estimation Bias in First-Order Dynamic Regression Models

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Author Info
Kiviet, J.F.
Phillips, G.D.A.

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Abstract

An approximation to order T-2 is obtained for the bias of the least-squares estimator in the stationary ARX model which yields generalisations of Kendall's and White's classic results for particular variants of AR(1) models. The results show that generally the second-order approximation is considerably better than its first-order counterpart in ARX models. This is also largely true for AR(1) models except that in such models second-order approximations may be vulnerable in the near unit root case.

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Publisher Info
Paper provided by University of Exeter, School of Business and Economics in its series Discussion Papers with number 99/03.

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Length: 52 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:fth:exetec:99/03

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Related research
Keywords: UNIT ROOTS ; REGRESSION ANALYSIS ; ECONOMETRICS;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Jan F. Kiviet & Garry D. A. Phillips, 2000. "Improved Coefficient and Variance Estimation in Stable First-Order Dynamic Regression Models," Econometric Society World Congress 2000 Contributed Papers 0631, Econometric Society. [Downloadable!]
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