Likelihood-Based Confidence Sets for the Timing of Structural Breaks

Abstract

In this paper, we propose a new approach to constructing confidence sets for the timing of structural breaks. This approach involves using Markov-chain Monte Carlo methods to simulate marginal “fiducial” distributions of break dates from the likelihood function. We compare our proposed approach to asymptotic and bootstrap confidence sets and find that it performs best in terms of producing short confidence sets with accurate coverage rates. Our approach also has the advantages of i) being broadly applicable to different patterns of structural breaks, ii) being computationally efficient, and iii) requiring only the ability to evaluate the likelihood function over parameter values, thus allowing for many possible distributional assumptions for the data. In our application, we investigate the nature and timing of structural breaks in postwar U.S. Real GDP. Based on marginal fiducial distributions, we find much tighter 95% confidence sets for the timing of the so-called “Great Moderation” than has been reported in previous studies.Fiducial Inference; Bootstrap Methods; Structural Breaks; Confidence Intervals and Sets; Coverage Accuracy and Expected Length; Markov-chain Monte Carlo;