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

Jan Kiviet and Garry Phillips

Discussion Papers from University of Exeter, Department of Economics

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.

Keywords: UNIT ROOTS; REGRESSION ANALYSIS; ECONOMETRICS (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Pages: 52 pages
Date: 1999
References: Add references at CitEc
Citations: View citations in EconPapers (3)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:exe:wpaper:9903

Access Statistics for this paper

More papers in Discussion Papers from University of Exeter, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sebastian Kripfganz ().