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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.

We investigate the finite sample behaviour of the ordinary least squares (OLS) estimator in vector autoregressive (VAR) models. The data generating process is assumed to be a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization we derive response surfaces for OLS bias and variance in terms of VAR dimensions both under correct model specification and under several types of over-parameterization: we include a constant, a constant and trend, and introduce excess autoregressive lags. Correction factors are introduced that minimise the mean squared error (MSE) of the OLS estimator. Our analysis improves and extends one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (1999), generalises the univariate variance and MSE results of Abadir (1995) to a multivariate setting, and complements various asymptotic studies. The distribution of unit root test statistics generally contains nuisance parameters that correspond to the correlation structure of the innovation errors. The presence of such nuisance parameters can lead to serious size distortions. To address this issue, we adopt an approach based on the characterization of the class of asymptotically similar critical regions for the unit root hypothesis and the application of two new optimality criteria for the choice of a test within this class. The correlation structure of the innovation sequence takes the form of a moving average process, the order of which is determined by an appropriate information criterion. Limit distribution theory for the resulting test statistics is developed and simulation evidence suggests that our statistics have substantially reduced size while retaining good power properties. Stock return predictability is a fundamental issue in asset pricing. The conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic variables considered as

We investigate the finite sample behaviour of the ordinary least squares (OLS) estimator in vector autoregressive (VAR) models. The data generating process is assumed to be a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization we derive response surfaces for OLS bias and variance in terms of VAR dimensions both under correct model specification and under several types of over-parameterization: we include a constant, a constant and trend, and introduce excess autoregressive lags. Correction factors are introduced that minimise the mean squared error (MSE) of the OLS estimator. Our analysis improves and extends one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (1999), generalises the univariate variance and MSE results of Abadir (1995) to a multivariate setting, and complements various asymptotic studies. The distribution of unit root test statistics generally contains nuisance parameters that correspond to the correlation structure of the innovation errors. The presence of such nuisance parameters can lead to serious size distortions. To address this issue, we adopt an approach based on the characterization of the class of asymptotically similar critical regions for the unit root hypothesis and the application of two new optimality criteria for the choice of a test within this class. The correlation structure of the innovation sequence takes the form of a moving average process, the order of which is determined by an appropriate information criterion. Limit distribution theory for the resulting test statistics is developed and simulation evidence suggests that our statistics have substantially reduced size while retaining good power properties. Stock return predictability is a fundamental issue in asset pricing. The conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic variables considered as

We investigate the finite sample behaviour of the ordinary least squares (OLS) estimator in vector autoregressive (VAR) models. The data generating process is assumed to be a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization we derive response surfaces for OLS bias and variance in terms of VAR dimensions both under correct model specification and under several types of over-parameterization: we include a constant, a constant and trend, and introduce excess autoregressive lags. Correction factors are introduced that minimise the mean squared error (MSE) of the OLS estimator. Our analysis improves and extends one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (1999), generalises the univariate variance and MSE results of Abadir (1995) to a multivariate setting, and complements various asymptotic studies. The distribution of unit root test statistics generally contains nuisance parameters that correspond to the correlation structure of the innovation errors. The presence of such nuisance parameters can lead to serious size distortions. To address this issue, we adopt an approach based on the characterization of the class of asymptotically similar critical regions for the unit root hypothesis and the application of two new optimality criteria for the choice of a test within this class. The correlation structure of the innovation sequence takes the form of a moving average process, the order of which is determined by an appropriate information criterion. Limit distribution theory for the resulting test statistics is developed and simulation evidence suggests that our statistics have substantially reduced size while retaining good power properties. Stock return predictability is a fundamental issue in asset pricing. The conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic variables considered as

We investigate the finite sample behaviour of the ordinary least squares (OLS) estimator in vector autoregressive (VAR) models. The data generating process is assumed to be a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization we derive response surfaces for OLS bias and variance in terms of VAR dimensions both under correct model specification and under several types of over-parameterization: we include a constant, a constant and trend, and introduce excess autoregressive lags. Correction factors are introduced that minimise the mean squared error (MSE) of the OLS estimator. Our analysis improves and extends one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (1999), generalises the univariate variance and MSE results of Abadir (1995) to a multivariate setting, and complements various asymptotic studies. The distribution of unit root test statistics generally contains nuisance parameters that correspond to the correlation structure of the innovation errors. The presence of such nuisance parameters can lead to serious size distortions. To address this issue, we adopt an approach based on the characterization of the class of asymptotically similar critical regions for the unit root hypothesis and the application of two new optimality criteria for the choice of a test within this class. The correlation structure of the innovation sequence takes the form of a moving average process, the order of which is determined by an appropriate information criterion. Limit distribution theory for the resulting test statistics is developed and simulation evidence suggests that our statistics have substantially reduced size while retaining good power properties. Stock return predictability is a fundamental issue in asset pricing. The conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic variables considered as

We investigate the finite sample behaviour of the ordinary least squares (OLS) estimator in vector autoregressive (VAR) models. The data generating process is assumed to be a purely nonstationary first-order VAR. Using Monte Carlo simulation and numerical optimization we derive response surfaces for OLS bias and variance in terms of VAR dimensions both under correct model specification and under several types of over-parameterization: we include a constant, a constant and trend, and introduce excess autoregressive lags. Correction factors are introduced that minimise the mean squared error (MSE) of the OLS estimator. Our analysis improves and extends one of the main finite-sample multivariate analytical bias results of Abadir, Hadri and Tzavalis (1999), generalises the univariate variance and MSE results of Abadir (1995) to a multivariate setting, and complements various asymptotic studies. The distribution of unit root test statistics generally contains nuisance parameters that correspond to the correlation structure of the innovation errors. The presence of such nuisance parameters can lead to serious size distortions. To address this issue, we adopt an approach based on the characterization of the class of asymptotically similar critical regions for the unit root hypothesis and the application of two new optimality criteria for the choice of a test within this class. The correlation structure of the innovation sequence takes the form of a moving average process, the order of which is determined by an appropriate information criterion. Limit distribution theory for the resulting test statistics is developed and simulation evidence suggests that our statistics have substantially reduced size while retaining good power properties. Stock return predictability is a fundamental issue in asset pricing. The conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic variables considered as