The Estimation of Threshold Models in Price Transmission Analysis

Friederike Greb, Stephan von Cramon-Taubadel, Tatyana Krivobokova and Axel Munk

American Journal of Agricultural Economics, 2013, vol. 95, issue 4, 900-916

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. Copyright 2013, Oxford University Press.

Date: 2013
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Working Paper: The estimation of threshold models in price transmission analysis (2012)
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