The finite-sample effects of VAR dimensions on OLS bias, OLS variance, and minimum MSE estimators
| Autor: | Michalis P. Stamatogiannis, Steve Lawford |
|---|---|
| Přispěvatelé: | ENAC - Laboratoire d'Economie et d'Econométrie de l'Aérien (LEEA), Ecole Nationale de l'Aviation Civile (ENAC), School of Economics, University of Nottingham, UK (UON), Department of Business Studies, Philips College |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Analysis of covariance
Economics and Econometrics Minimum mean square error C15 C22 C32 [Finite-sample bias Monte Carlo simulation Nonstationary time series Response surface Vector autoregression JEL classification] Mean squared error Economics Applied Mathematics 05 social sciences Univariate Wirtschaft Estimator [SHS.ECO]Humanities and Social Sciences/Economics and Finance Autoregressive model Wirtschaftsstatistik Ökonometrie Wirtschaftsinformatik 0502 economics and business Statistics Ordinary least squares ddc:330 Econometrics Social Sciences & Humanities 050207 economics Economic Statistics Econometrics Business Informatics 050205 econometrics Mathematics |
| Zdroj: | Econometrics Econometrics, MDPI, 2009, 148 (2), pp 124-130. ⟨10.1016/j.jeconom.2008.10.004⟩ Journal of Econometrics |
| ISSN: | 2225-1146 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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