PRACTITIONERS CORNER: A Simple Test for Cointegration

Applications of Mathematics

1992

Journal of Time Series Analysis, 1997

A residual-based test for cointegration is proposed where a parametric adjustment is made to account for the possible stationarity of the disturbance vector. Allowance is also made for the regressor variables to be cointegrated among themselves. The parametric adjustment turns out to be more robust and powerful than tests based on long-run variance estimators according to theoretical and simulation evidence.

2003

In this paper we generate critical values for a test for cointegration based on the joint significance of the levels terms in an error correction equation. We show that the appropriate critical values are higher than those derived from the standard F-distribution. We compare the power properties of this test with those of the Engle-Granger test and Kremers et al's t-test based on the t-statistic from an error correction equation. The F-test has higher power than the Engle-Granger test but lower power than the t-form of the error correction test. However, the F-form of the test has the advantage that its distribution is independent of the parameters of the problem being considered. Finally, we consider a test for cointegration between UK and US interest rates. We show that the F-test rejects the null of no cointegration between these variables although the Engle-Granger test fails to do so.

2003

Part 1. Automated model selection Contents Chapter 2. Automatic identification of simultaneous equations models 2.1. Introduction 22. Results 2.3, Algorithm 2.4. An application 2.5. Conclusions 2;A. Proofs 2.B. Matlab program Chapter 3. Automatic identification and restriction of the cointegration space 3d. Introduction 3.2. The cointegrated VAR model 3.3. Identification and restriction of (3 3.4. Monte Carlo evidence 3.5. Use of the algorithm 3.6. Conclusions 3;A. Proofs Part 2. Small sample corrections Chapter 4. Bootstrapping and Bartlett corrections in the cointegrated VAR model 4.1. Introduction 4;2. Bartlett-corrected and bootstrap tests on cointegrating coefficients 4.3. Design of the Monte Carlo experiment 4.4. Results 4.5. Conclusions Chapter 5. A Bartlett correction in stationary autoregressive models Omtzigt, Pieter (2003), Essays on Cointegration Analysis

Journal of Time Series Analysis, 2002

In this paper we introduce a new test of the null hypothesis of no cointegration between a pair of time series. For a very simple generating model, our test compares favourably with the Engle-Granger/Dickey-Fuller test and the Johansen trace test. Indeed, shortcomings of the former motivated the development of our test.

Recent papers by Charemza and Syczewska (1998) and Carrion, Sansó and Ortuño (2001) focused on the joint use of unit root and stationarity tests. In this paper, the discussion is extended to the case of cointegration. Critical values for testing the joint con…rmation hypothesis of no cointegration are computed and a small Monte Carlo experiment evaluates the relative performance of this procedure.

Research Memorandum, 1998

This paper provides an extensive Monte-Carlo comparison of sev-eral contemporary cointegration tests. Apart from the familiar Gaus-sian based tests of Johansen, we also consider tests based on non-Gaussian quasi-likelihoods. Moreover, we compare the performance ...

2000

'Classical' econometric theory assumes that observed data come from a stationary process, where means and variances are constant over time. Graphs of economic time series, and the historical record of economic forecasting, reveal the invalidity of such an assumption. Consequently, we discuss the importance of stationarity for empirical modeling and inference; describe the effects of incorrectly assuming stationarity; explain the basic concepts of non-stationarity; note some sources of non-stationarity; formulate a class of non-stationary processes (autoregressions with unit roots) that seem empirically relevant for analyzing economic time series; and show when an analysis can be transformed by means of differencing and cointegrating combinations so stationarity becomes a reasonable assumption. We then describe how to test for unit roots and cointegration. Monte Carlo simulations and empirical examples illustrate the analysis. * Financial support from the U.K. Economic and Social Research Council under grant R000234954, and from the Danish Social Sciences Research Council is gratefully acknowledged. We are pleased to thank Campbell Watkins for helpful comments on, and discussion of, earlier drafts. 1 Blocks of four graphs are lettered notionally as a, b; c, d in rows from the top left; six graphs are a, b, c; d, e, f; and so on.

Economics Letters, 1995