Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models

Jan Kiviet and Garry Phillips

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3705-3729

Abstract: An approximation to order T−2 is obtained for the bias of the full vector of least-squares estimates obtained from a sample of size T in general stable but not necessarily stationary ARX(1) models with normal disturbances. This yields generalizations, allowing for various forms of initial conditions, of Kendall’s and White’s classic results for stationary AR(1) models. The accuracy of various alternative approximations is examined and compared by simulation for particular parameterizations of AR(1) and ARX(1) models. The results show that often the second-order approximation is considerably better than its first-order counterpart and hence opens up perspectives for improved bias correction. However, order T−2 approximations are also found to be more vulnerable in the near unit root case than the much simpler order T−1 approximations.

Keywords: ARX-model; Asymptotic expansion; Bias approximation; Lagged dependent variable; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Higher-Order Asymptotic Expansions of the Least-Squares Estimation Bias in First-Order Dynamic Regression Models (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:11:p:3705-3729

DOI: 10.1016/j.csda.2010.07.013

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