The Finite-Sample E ects of VAR Dimensions on OLS Bias, OLS Variance, and Minimum MSE Estimators

Steve Lawford and Michalis P. Stamatogiannis

Working Paper series from Rimini Centre for Economic Analysis

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 optimization to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct specification and several types of over-parameterization 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 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 (Econometrica 67 (1999) 163), generalize the univariate variance and MSE findings of Abadir (Economics Letters 47 (1995) 263) to the multivariate setting, and complement various asymptotic studies.

Keywords: Finite-sample bias; Monte Carlo simulation; nonstationary time series; response surfaces; vector autoregression (search for similar items in EconPapers)
JEL-codes: C15 C22 C32 (search for similar items in EconPapers)
Date: 2008-01
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Related works:
Journal Article: The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators (2009)
Working Paper: The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:13_08

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