IDEAS home Printed from https://ideas.repec.org/p/cpb/discus/284.html
   My bibliography  Save this paper

Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model

Author

Listed:
  • Arie ten Cate

Abstract

This CPB Discussion Paper merges two isolated bodies of literature: the Markov chain model with macro data (MacRae, 1977) and the ecological inference model (Robinson, 1950). Both are choice models. They have the same likelihood function and the same regression equation. Decades ago, this likelihood function was computationally demanding. This has led to the use of several approximate methods. Due to the improvement in computer hardware and software since 1977, exact maximum likelihood should now be the preferred estimation method. CPB Discussion Paper 284, "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model" 15 september 2014 is published in : Journal of Economic and Social Measurement, vol. 43, no. 1-2, pp. 1-9, 2018.

Suggested Citation

  • Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284, CPB Netherlands Bureau for Economic Policy Analysis.
    • Handle: RePEc:cpb:discus:284
    as

    Download full text from publisher

    File URL: https://www.cpb.nl/sites/default/files/publicaties/download/cpb-discussion-paper-284.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Pelzer & Rob Eisinga & Philip Hans Franses, 2001. "Estimating Transition Probabilities from a Time Series of Independent Cross Sections," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(2), pages 249-262, July.
    2. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
    3. Warren Dent & Richard Ballintine, 1971. "A Review Of The Estimation Of Transition Probabilities In Markov Chains," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 15(2), pages 69-81, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    2. Hugo Storm & Thomas Heckelei & Ron C. Mittelhammer, 2016. "Bayesian estimation of non-stationary Markov models combining micro and macro data," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 303-329.
    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. Villacorta, Pablo J. & Verdegay, José L., 2016. "FuzzyStatProb: An R Package for the Estimation of Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i08).
    5. Pasanisi, Alberto & Fu, Shuai & Bousquet, Nicolas, 2012. "Estimating discrete Markov models from various incomplete data schemes," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2609-2625.
    6. Jeffrey M. Gillespie & Joan R. Fulton, 2001. "A Markov chain analysis of the size of hog production firms in the United States," Agribusiness, John Wiley & Sons, Ltd., vol. 17(4), pages 557-570.
    7. Melanie Parravano & Luis Enrique Pedauga, 2008. "Oil market dynamics: A Markow chain analysis," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 33(25), pages 87-115, january-j.
    8. 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.
    9. Zepeda, Lydia & Chavas, Jean-Paul, 1991. "The Structure of Wisconsin Milk production: a Dynamic Analysis," WAEA/ WFEA Conference Archive (1929-1995) 321485, Western Agricultural Economics Association.
    10. Jacob Fiksel & Scott Zeger & Abhirup Datta, 2022. "A transformation‐free linear regression for compositional outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(3), pages 974-987, September.
    11. Silke Huettel & Anne Margarian, 2009. "Structural change in the West German agricultural sector," Agricultural Economics, International Association of Agricultural Economists, vol. 40(s1), pages 759-772, November.
    12. Arkadiusz Manikowski, 2021. "The Markov Process as a Model of Migration Based on the Example of the Movement of Banknotes (Proces Markowa jako model migracji na przykladzie przemieszczania sie banknotow)," Research Reports, University of Warsaw, Faculty of Management, vol. 2(35), pages 76-92.
    13. Libbin, James D., 1982. "Projections of US farm numbers by Markov processes," ISU General Staff Papers 198201010800008508, Iowa State University, Department of Economics.
    14. Barsotti, Flavia & De Castro, Yohann & Espinasse, Thibault & Rochet, Paul, 2014. "Estimating the transition matrix of a Markov chain observed at random times," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 98-105.
    15. Love, H. Alan & Karp, Larry, 1988. "The Effect of Anticipated Farm Failure on the.Agricultural Land Market," 1988 Annual Meeting, August 1-3, Knoxville, Tennessee 270186, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    16. Eldosouky, AbdelRahman & Saad, Walid & Mandayam, Narayan, 2021. "Resilient critical infrastructure: Bayesian network analysis and contract-Based optimization," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    17. 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.
    18. Davor Kunovac, 2011. "Estimating Credit Migration Matrices with Aggregate Data – Bayesian Approach," Working Papers 30, The Croatian National Bank, Croatia.
    19. Enrico Fabrizi & Gianni Guastella & Stefano Marta & Francesco Timpano, 2016. "Determinants of Intra-Distribution Dynamics in European Regions: An Empirical Assessment of the Role of Structural Intervention," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 107(5), pages 522-539, December.
    20. Jongeneel, Roelof A. & Longworth, Natasha & Huettel, Silke, 2005. "Dairy Farm Size Distribution in East and West: Evolution and Sensitivity to Structural and Policy Variables: Case-Studies of the Netherlands, Germany, Poland and Hungary," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24772, European Association of Agricultural Economists.

    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpb:discus:284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cpbgvnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.