Testing for Cointegration in Linear Quadratic Models

Allan W. Gregory

94 citations0/0 files storedJournal of Business and Economic Statistics

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Abstract

This article evaluates the finite-sample performance of various tests for cointegration by Monte Carlo methods. The evaluation takes place within the linear quadratic model. The results indicate sharp differences in the ability of the tests to detect cointegrating relations, especially when the cost-of-adjustment term and the number of regressors are large. Although no single test dominates for all the parameter settings considered, overall the augmented Dickey-Fuller, Z α and Zt tests of Phillips seem the most reliable in terms of test size and power.

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References

1987Co-Integration and Error Correction: Representation, Estimation, and Testing 1988Testing for a Unit Root in Time Series Regression 1988Statistical analysis of cointegration vectors 1990MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY 1991Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models 1993A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems 1990Statistical Inference in Instrumental Variables Regression with I(1) Processes 1991Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

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