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Information Recovery in a Dynamic Statistical Markov Model

Author

Listed:
  • Douglas J. Miller

    (Economics and Management of Agrobiotechnology Center, University of Missouri, Columbia, MO 65211, USA)

  • George Judge

    (Graduate School, 207 Giannini Hall, University of California, Berkeley, Berkeley, CA 94720, USA)

Abstract

Although economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence information associated with the underlying dynamic micro-behavior. Estimating equations are used as a link to the data and to model the dynamic conditional Markov process. To recover the unknown transition probabilities, we use an information theoretic approach to model the data and derive a new class of conditional Markov models. A quadratic loss function is used as a basis for selecting the optimal member from the family of possible likelihood-entropy functional(s). The asymptotic properties of the resulting estimators are demonstrated, and a range of potential applications is discussed.

Suggested Citation

  • Douglas J. Miller & George Judge, 2015. "Information Recovery in a Dynamic Statistical Markov Model," Econometrics, MDPI, vol. 3(2), pages 1-12, March.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:2:p:187-198:d:47332
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    References listed on IDEAS

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    2. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    3. MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
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    5. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    6. Theil, Henri, 1969. "A Multinomial Extension of the Linear Logit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 10(3), pages 251-259, October.
    7. Gospodinov, Nikolay & Lkhagvasuren, Damba, 2011. "A new method for approximating vector autoregressive processes by finite-state Markov chains," MPRA Paper 33827, University Library of Munich, Germany.
    8. Tanaka, Ken’ichiro & Toda, Alexis Akira, 2013. "Discrete approximations of continuous distributions by maximum entropy," Economics Letters, Elsevier, vol. 118(3), pages 445-450.
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    Cited by:

    1. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    2. George Judge, 2016. "Econometric Information Recovery in Behavioral Networks," Econometrics, MDPI, vol. 4(3), pages 1-11, September.
    3. Judge, George, 2023. "Information Recovery in Complex Economic Systems," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4jj70102, Department of Agricultural & Resource Economics, UC Berkeley.
    4. George Judge, 2018. "Micro-Macro Connected Stochastic Dynamic Economic Behavior Systems," Econometrics, MDPI, vol. 6(4), pages 1-14, December.
    5. Maples, Chellie H. & Hagerman, Amy D. & Lambert, Dayton M., 2022. "Ex-ante effects of the 2018 Agricultural Improvement Act’s grassland initiative," Land Use Policy, Elsevier, vol. 116(C).

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