The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators

Abstract: Vector autoregressions (VARs) are important tools in time series analysis. However, relatively little is known about the finite-sample behaviour of parameter estimators. We address this issue, by investigating ordinary least squares (OLS) estimators given a data generating process that is a purely nonstationary first-order VAR. Specifically, we use Monte Carlo simulation and numerical optimisation to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct specification and several types of over-parameterisation of the model: we include a constant, and a constant and trend, and introduce excess lags. We then examine the correction factors that are required for the least squares estimator to attain the minimum mean squared error (MSE). Our results improve and extend one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis [Abadir, K.M., Hadri, K., Tzavalis, E., 1999. The influence of VAR dimensions on estim

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Postprint ; begutachtet (peer reviewed) ; In: Journal of Econometrics ; 148 (2009) 2 ; 124-130

Classification and Topics

• Classification: Wirtschaft

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (when): 2009
• Creator: Lawford, Steve ; Stamatogiannis, Michalis P.

Further information

• DOI: 10.1016/j.jeconom.2008.10.004
• URN: urn:nbn:de:0168-ssoar-215759
• Rights: Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 25.03.2025, 1:50 PM CET