Cointegration Testing Using Pseudolikelihood Ratio Tests

André Lucas

46 citations0/0 files storedEconometric Theory

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Abstract

This paper considers pseudomaximum likelihood estimators for vector autoregressive models. These estimators are used to determine the cointegration rank of a multivariate time series process using pseudolikelihood ratio tests. The asymptotic distributions of these tests depend on nuisance parameters if the pseudolikelihood is non-Gaussian. This even holds if the likelihood is correctly specified. The nuisance parameters have a natural interpretation and can be consistently estimated. Some simulation results illustrate the usefulness of the tests: non-Gaussian pseudolikelihood ratio tests generally have a higher power than the Gaussian test of Johansen if the innovations demonstrate leptokurtic behavior.

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References

1987Co-Integration and Error Correction: Representation, Estimation, and Testing 1988Statistical analysis of cointegration vectors 1991Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models 1987Time Series Regression with a Unit Root 1992Canonical Cointegrating Regressions 1991Optimal Inference in Cointegrated Systems 1986Multiple Time Series Regression with Integrated Processes 1988Statistical Inference in Regressions with Integrated Processes: Part 1

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2009Long‐run relations in European electricity prices 2001Estimation for partially nonstationary multivariate autoregressive models with conditional heteroscedasticity 2000Testing for a Unit Root with Near-Integrated Volatility 2000Cointegration Testing Under Structural Breaks: A Robust Extended Error Correction Model 2005Distribution approximations for cointegration tests with stationary exogenous regressors 1998

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