The estimation of threshold models in price transmission analysis

The threshold vector error correction model is a popular tool for the analysis of spatial price transmission and market integration. In the literature, the profi le likelihood estimator is the preferred choice for estimating this model. Yet, in certain 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 diff er little between regimes, the profi le likelihood estimator displays large bias and variance. Such settings are likely when studying price transmission. For simpler, but related threshold models Greb et al. (2011) have developed an alternative estimator, the regularized Bayesian estimator, which does not exhibit these weaknesses. We explore the properties of this estimator for threshold vector error correction models. Simulation results show that it outperforms the profi le likelihood estimator, especially in situations in which the pro file likelihood estimator fails. Two empirical applications - a reassessment of the the seminal paper by Goodwin and Piggott (2001), and an analysis of price transmission between German and Spanish markets for pork - demonstrate the relevance of the new approach for spatial price transmission analysis.