Abstract
The threshold vector error correction model is a popular tool for the analysis of spatial price transmission. In the literature, the profile likelihood estimator is the preferred choice for estimating this model. Yet, in many settings this estimator performs poorly. In particular, if the true thresholds are such that one or more regimes contain only a small number of observations, if unknown model parameters are numerous, or if parameters differ little between regimes, the profile likelihood estimator displays large bias and variance. Such settings are likely when studying price transmission. We analyze the weaknesses of the profile likelihood approach and propose an alternative regularized Bayesian estimator, which was developed for simpler but related threshold models. Simulation results show that the regularized Bayesian estimator outperforms profile likelihood in the estimation of threshold vector error correction models. Two empirical applications demonstrate the relevance of this new estimator for spatial price transmission analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
