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

[Zeitschriftenartikel]

Lawford, Steve; Stamatogiannis, Michalis P.

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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 estimator biases. Econometrica 67, 163–181], generalise the univariate variance and MSE findings of Abadir [Abadir, K.M., 1995. Unbiased estimation as a solution to testing for random walks. Economics Letters 47, 263–268] to the multivariate setting, and complement various asymptotic studies.
Klassifikation Wirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatik
Freie Schlagwörter Finite-sample bias; Monte Carlo simulation; Nonstationary time series; Response surface; Vector autoregression; JEL classification: C15; C22; C32
Sprache Dokument Englisch
Publikationsjahr 2009
Seitenangabe S. 124-130
Zeitschriftentitel Journal of Econometrics, 148 (2009) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2008.10.004
Status Postprint; begutachtet (peer reviewed)
Lizenz PEER Licence Agreement (applicable only to documents from PEER project)
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