Confidence Sets for the Date of a Structural Change at the End of a Sample
Journal of Time Series Analysis, 2018, vol. 39, issue 6, 850-862
This paper proposes the construction of a confidence set for the date of a structural change at the end of a sample in a linear regression model. While the break fraction, that is, the ratio of the number of observations before the break to the sample size, is typically assumed to take a value in the (0,1) open interval, we consider the case where a permissible break date is included in a fixed number of observations at the end of the sample; thus the break fraction approaches 1 as the sample size goes to infinity. We propose inverting the test for the break date to construct a confidence set while obtaining the critical values by using the subsampling method. By using Monte Carlo simulations, we show that the confidence set proposed in this paper can control the coverage rate in finite samples well while the average length of the confidence set is comparable to existing methods based on asymptotic theory with a fixed break fraction in the (0,1) interval.
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