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We propose the use of likelihood-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about the...
Persistent link: https://www.econbiz.de/10013082120
This paper investigates by means of Monte Carlo techniques the robustness of the CUSUM and CUSUM-of-squares tests (Brown et al., 1975) to serial correlation, endogeneity and lack of structural invariance. Our findings suggest that these tests perform better in the context of a dynamic model of...
Persistent link: https://www.econbiz.de/10009728982
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To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a...
Persistent link: https://www.econbiz.de/10011297656
Prior studies have proposed constructing confidence sets for the break date by inverting a sequence of tests for the date of a structural break. In this study, we improve these confidence sets by taking the direction of the break into account. Even when the break direction is unknown, we can...
Persistent link: https://www.econbiz.de/10012849388
We propose the use of likelihood-ratio-based confidence sets for the timing of structural breaks in parameters from time series regression models. The confidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about...
Persistent link: https://www.econbiz.de/10011757721
We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...
Persistent link: https://www.econbiz.de/10011523575
This paper demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests - the ADF, Phillips-Perron, and DF-GLS tests - frequently overreject the true null of a unit...
Persistent link: https://www.econbiz.de/10011309691
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