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• W1561844880 abstract "Vector autoregressions (VARs) are an important tool in time series analysis. However, relatively little is known about the …nite-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 …rst-order VAR. Speci…cally, we use Monte Carlo simulation and numerical optimization to derive response surfaces for OLS bias and variance, in terms of VAR dimensions, given correct 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 required for the least squares estimator to attain minimum mean squared error (MSE). Our results improve and extend one of the main …nite-sample analytical bias results of Abadir, Hadri and Tzavalis (Econometrica 67 (1999) 163), generalize the univariate variance and MSE …ndings of Abadir (Econ. Lett. 47 (1995) 263), and complement various asymptotic studies." @default.
• W1561844880 created "2016-06-24" @default.
• W1561844880 creator A5029353080 @default.
• W1561844880 creator A5080597599 @default.
• W1561844880 date "2004-03-01" @default.
• W1561844880 modified "2023-09-27" @default.
• W1561844880 title "The Finite-Sample Effects of VAR Dimensions on OLS Bias, OLS Variance, and Minimum MSE Estimators: Purely Nonstationary Case" @default.
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• W1561844880 hasPublicationYear "2004" @default.
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