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Bayesian estimation of non-stationary Markov models combining micro and macro data


  • Storm, Hugo
  • Heckelei, Thomas


In this poster a Bayesian estimation framework for a non-stationary Markov model is developed for situations where sample data with observed transition between classes (micro data) and aggregate population shares (macro data) are available. Posterior distributions on transition probabilities are derived based on a micro based prior and a macro based Likelihood function thereby consistently combining previously separated approaches. Monte Carlo simulations for ordered and unordered Markov states show how observed micro transitions improve precision of posterior knowledge as the sample size increases.

Suggested Citation

  • Storm, Hugo & Heckelei, Thomas, 2011. "Bayesian estimation of non-stationary Markov models combining micro and macro data," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103645, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea11:103645

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    References listed on IDEAS

    1. Andrea Zimmermann & Thomas Heckelei, 2012. "Structural Change of European Dairy Farms – A Cross-Regional Analysis," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(3), pages 576-603, September.
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    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, April.
    7. Silke Huettel & Roel Jongeneel, 2011. "How has the EU milk quota affected patterns of herd-size change?," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 38(4), pages 497-527, October.
    8. Ben Pelzer, 2002. "Bayesian estimation of transition probabilities from repeated cross sections," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 23-33.
    9. Andrés Musalem & Eric T. Bradlow & Jagmohan S. Raju, 2009. "Bayesian estimation of random‐coefficients choice models using aggregate data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 490-516, April.
    10. Heckelei, Thomas & Mittelhammer, Ronald C. & Jansson, Torbjorn, 2008. "A Bayesian Alternative To Generalized Cross Entropy Solutions For Underdetermined Econometric Models," Discussion Papers 56973, University of Bonn, Institute for Food and Resource Economics.
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    13. Robert A. Jarrow & David Lando & Stuart M. Turnbull, 2008. "A Markov Model for the Term Structure of Credit Risk Spreads," World Scientific Book Chapters,in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 18, pages 411-453 World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Storm, Hugo & Heckelei, Thomas, 2012. "Predicting agricultural structural change using census and sample data," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125185, Agricultural and Applied Economics Association.
    2. Oudendag, Diti & Hoogendoorn, Mark & Jongeneel, Roel, 2014. "Agent-Based Modeling of Farming Behavior: A Dutch Case Study on Milk Quota Abolishment and Sustainable Dairying," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182700, European Association of Agricultural Economists.
    3. Neuenfeldt, S. & Rieger, J. & Heckelei, T. & Gocht, A. & Ciaian, P. & Tetteh, G., 2018. "A multiplicative competitive interaction model to explain structural change along farm specialisation, size and exit/entry using Norwegian farm census data," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277090, International Association of Agricultural Economists.
    4. Legrand D. F. Saint-Cyr & Laurent Piet, 2017. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 777-795, August.
    5. Laurent, Piet & Legrand D.F. Saint-Cyr, 2016. "Projection de la population des exploitations agricoles françaises à l’horizon 2025," Working Papers SMART - LERECO 16-11, INRA UMR SMART-LERECO.
    6. Saint-Cyr, Legrand D. F. & Piet, Laurent, 2014. "Movers and Stayers in the Farming Sector: Another Look at Heterogeneity in Structural Change," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183068, European Association of Agricultural Economists.

    More about this item


    Agricultural and Food Policy; Research Methods/ Statistical Methods;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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