A method developed by Bai and Perron (2003) also allows for the detection of multiple structural breaks from data. The sup-Wald, sup-LM, and sup-LR tests are asymptotic in general (i.e., the asymptotic critical values for these tests are applicable for sample size n as n → ∞), and involve the assumption of homoskedasticity across break points for finite samples however, an exact test with the sup-Wald statistic may be obtained for a linear regression model with a fixed number of regressors and independent and identically distributed (IID) normal errors. These tests were shown to be superior to the CUSUM test in terms of statistical power, and are the most commonly used tests for the detection of structural change involving an unknown number of breaks in mean with unknown break points. For cases 1 and 2, the sup-Wald (i.e., the supremum of a set of Wald statistics), sup-LM (i.e., the supremum of a set of Lagrange multiplier statistics), and sup-LR (i.e., the supremum of a set of likelihood ratio statistics) tests developed by Andrews (1993, 2003) may be used to test for parameter instability when the number and location of structural breaks are unknown. In general, the CUSUM (cumulative sum) and CUSUM-sq (CUSUM squared) tests can be used to test the constancy of the coefficients in a model. The Chow test is not applicable in these situations, since it only applies to models with a known breakpoint and where the error variance remains constant before and after the break. Other forms of structural breaks Ĭase 1: a known number of breaks in mean with unknown break points Case 2: an unknown number of breaks in mean with unknown break points Case 3: breaks in variance. This test assesses whether the coefficients in a regression model are the same for periods and. Structural break tests A single break in mean with a known breakpoint įor linear regression models, the Chow test is often used to test for a single break in mean at a known time period K for K ∈ .
When a break is present, the limit distribution of the test is the same as in the case of a known break date, allowing increased power while maintaining the correct size.
Kim and Perron (2009, Journal of Econometrics 148, 1–13) developed a methodology that allows a break at an unknown time under both the null and alternative hypotheses. However, in doing so most research, in particular the commonly used test of Zivot and Andrews (1992, Journal of Business & Economic Statistics 10, 251–270), assumed that if a break occurs it does so only under the alternative hypothesis of stationarity. The subsequent literature devised procedures valid in the case of an unknown break date. In particular, they allow a break under both the null and alternative hypotheses and are invariant to the magnitude of the shift in level and/or slope.
#PERRON 1989 STRUCTURAL BREAK TEST IN EVIEWS 9 SERIES#
Perron (1989, Econometrica 57, 1361–1401) introduced unit root tests valid when a break at a known date in the trend function of a time series is present.
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