Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models

Jan Kiviet and Garry D.A. Phillips

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 424-448

Abstract: In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. Using expansion techniques an approximation is obtained to the bias in variance estimation yielding a bias corrected variance estimator. This is achieved for both the standard and a bias corrected coefficient estimator enabling a comparison of their mean squared errors to second order. Sufficient conditions for admissibility of these approximations are formally derived. Illustrative numerical and simulation results are presented on bias reduction of coefficient and variance estimation for three relevant classes of first-order autoregressive models, supplemented by effects on mean squared errors, test size and size corrected power. These indicate that substantial biases do occur in moderately large samples, but these can be mitigated considerably and may also yield mean squared error reduction. Crude asymptotic tests are cursed by huge size distortions. However, operational bias corrections of both the estimates of coefficients and their estimated variance (for which software is provided) are shown to curb type I errors reasonably well.

Keywords: Bias correction; Efficiency gains; Finite sample moments; Higher-order asymptotic expansions; Lagged dependent variables; Size improvements (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947313003459
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: Improved Variance Estimation of Maximum Likelihood Estimators in Stable 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:eee:csdana:v:76:y:2014:i:c:p:424-448

DOI: 10.1016/j.csda.2013.09.021

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().