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The estimation of threshold models in price transmission analysis


  • Friederike Greb

    (Georg-August-University Göttingen)

  • Stephan von Cramon-Taubadel

    (Georg-August-University Göttingen)

  • Tatyana Krivobokova

    (Georg-August-University Göttingen)

  • Axel Munk

    (Georg-August-University Göttingen)


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.

Suggested Citation

  • Friederike Greb & Stephan von Cramon-Taubadel & Tatyana Krivobokova & Axel Munk, 2011. "The estimation of threshold models in price transmission analysis," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 103, Courant Research Centre PEG, revised 08 Oct 2012.
  • Handle: RePEc:got:gotcrc:103

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    References listed on IDEAS

    1. Hansen, Bruce E. & Seo, Byeongseon, 2002. "Testing for two-regime threshold cointegration in vector error-correction models," Journal of Econometrics, Elsevier, vol. 110(2), pages 293-318, October.
    2. Barry K. Goodwin & Nicholas E. Piggott, 2001. "Spatial Market Integration in the Presence of Threshold Effects," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(2), pages 302-317.
    3. Lo, Ming Chien & Zivot, Eric, 2001. "Threshold Cointegration And Nonlinear Adjustment To The Law Of One Price," Macroeconomic Dynamics, Cambridge University Press, vol. 5(04), pages 533-576, September.
    4. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    5. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    6. Kelvin Balcombe & Alastair Bailey & Jonathan Brooks, 2007. "Threshold Effects in Price Transmission: The Case of Brazilian Wheat, Maize, and Soya Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 308-323.
    7. Kelvin Balcombe & George Rapsomanikis, 2008. "Bayesian Estimation and Selection of Nonlinear Vector Error Correction Models: The Case of the Sugar-Ethanol-Oil Nexus in Brazil," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(3), pages 658-668.
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    Cited by:

    1. William Hahn & Hayden Stewart & Donald P. Blayney & Christopher G. Davis, 2016. "Modeling price transmission between farm and retail prices: a soft switches approach," Agricultural Economics, International Association of Agricultural Economists, vol. 47(2), pages 193-203, March.

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    Bayesian estimator; market integration; spatial arbitrage; TVECM;

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