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...

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...

Within the context of threshold regressions, we show that asymptotically-valid likelihood-ratio-based confidence intervals for threshold parameters perform poorly in finite samples when the threshold effect is large. A large threshold effect leads to a poor approximation of the profile...

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...

An updated version of our Markov-switching model of U.S. real GDP clearly suggests the COVID-19 recession was more U shaped than L shaped. As with linear time series models, it is important to account for extreme outliers during the pandemic, but a simple decay function for volatility from...

Since the Great Recession in 2007-09, U.S. real GDP has failed to return to its previously projected path, a phenomenon widely associated with secular stagnation. We investigate whether this stagnation was due to hysteresis effects from the Great Recession, a persistent negative output gap...

We propose the use of likelihood-ratio-based con fidence sets for the timing of structural breaks in parameters from time series regression models. The con fidence sets are valid for the broad setting of a system of multivariate linear regression equations under fairly general assumptions about...