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

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  • Storm, Hugo
  • Heckelei, Thomas
  • Mittelhammer, Ron

Abstract

We develop a Bayesian estimation framework for non-stationary Markov models for situations where both sample data on observed transitions between states (micro data) and population data, where only the proportion of individuals in each state is observed (macro data), are available. Posterior distributions on transition probabilities are derived from a micro-based prior and a macrobased likelihood, thereby providing a new method that combines micro and macro information in a logically consistent manner and merges previously disparate approaches for inferring transition probabilities. Monte Carlo simulations for ordered and unordered states show how observed micro transitions improve the precision of posterior knowledge.

Suggested Citation

  • Storm, Hugo & Heckelei, Thomas & Mittelhammer, Ron, 2011. "Bayesian estimation of non-stationary Markov models combining micro and macro data," Discussion Papers 162894, University of Bonn, Institute for Food and Resource Economics.
  • Handle: RePEc:ags:ubfred:162894
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    References listed on IDEAS

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    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.
    2. Nejla Ben Arfa & Karine Daniel & Florence Jacquet & Kostas Karantininis, 2015. "Agricultural Policies and Structural Change in French Dairy Farms: A Nonstationary Markov Model," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 63(1), pages 19-42, March.
    3. Karantininis, Kostas, 2002. "Information-based estimators for the non-stationary transition probability matrix: an application to the Danish pork industry," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 275-290, March.
    4. D. L. Hawkins & Chien-Pai Han, 2000. "Estimating Transition Probabilities from Aggregate Samples Plus Partial Transition Data," Biometrics, The International Biometric Society, vol. 56(3), pages 848-854, September.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, December.
    6. 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.
    7. 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..
    8. Zimmermann, Andrea & Heckelei, Thomas, 2012. "Differences of farm structural change across European regions," Discussion Papers 162879, University of Bonn, Institute for Food and Resource Economics.
    9. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
    10. 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.
    11. 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.
    12. 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.
    13. Steven Scott, 2011. "Data augmentation, frequentist estimation, and the Bayesian analysis of multinomial logit models," Statistical Papers, Springer, vol. 52(1), pages 87-109, February.
    14. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700.
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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. 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.
    4. 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.
    5. 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

    Keywords

    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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