A Monte Carlo Study of the Effects of Structural Breaks on Tests for Unit Roots

The effects of a shift in the intercept of an autoregressive process on the rejection frequencies of standard tests for unit roots are investigated using Monte Carlo methods. Such tests lose power compared with the equivalent parameter values when no breaks occur. F-tests for structural breaks fail to detect shifts that are large enough to mimic unit roots. The response surface summarizing a conventional Monte Carlo highlights the effects on Dickey-Fuller (DF) and Augmented Dickey-Fuller (ADF) tests of the magnitudes of the autoregressive parameter, the break, the cumulative break, the estimation sample, and the percentage of the sample contaminated by the break. Diagnostic tests on the response surface support its specification. A recursive Monte Carlo computes sequences of rejection frequencies of DF and Chow tests and shows that these are low. Thus, care is required in interpreting unit-root tests since failure to reject does not entail that the null is true.

[1]  Alok Bhargava,et al.  Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk , 1983 .

[2]  P. Perron,et al.  The Great Crash, The Oil Price Shock And The Unit Root Hypothesis , 1989 .

[3]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[4]  Anil K. Bera,et al.  Efficient tests for normality, homoscedasticity and serial independence of regression residuals , 1980 .

[5]  P. Phillips,et al.  Does Gnp Have a Unit Root? a Reevaluation , 1986 .

[6]  Grayham E. Mizon,et al.  A note on the distribution of the least squares estimator of a random walk with drift , 1989 .

[7]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[8]  G. Chow Tests of equality between sets of coefficients in two linear regressions (econometrics voi 28 , 1960 .

[9]  David F. Hendry,et al.  Monte carlo experimentation in econometrics , 1984 .

[10]  O. Gulley Are saving and investment cointegrated?: Another look at the data , 1992 .

[11]  C. Nelson,et al.  Trends and random walks in macroeconmic time series: Some evidence and implications , 1982 .

[12]  Jean-Francois Richard,et al.  The Encompassing Principle and Its Application to Testing Non-nested Hypotheses , 1986 .

[13]  P. Phillips Testing for a Unit Root in Time Series Regression , 1988 .

[14]  H. White A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity , 1980 .

[15]  James G. MacKinnon,et al.  Critical Values for Cointegration Tests , 1990 .

[16]  K. West,et al.  Asymptotic normality, when regressors have a unit root , 1988 .

[17]  David R. Cox The analysis of binary data , 1970 .

[18]  Lucrezia Reichlin,et al.  Segmented trends and non-stationary time series , 1989 .

[19]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[20]  Jurgen A. Doornik,et al.  PcGive 8.0 : an interactive econometric modelling system , 1990 .

[21]  S. Miller Are saving and investment co-integrated? , 1988 .

[22]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .