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Information Recovery in a Dynamic Statistical Markov Model

Douglas J. Miller () and George Judge ()
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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

Econometrics, 2015, vol. 3, issue 2, 1-12

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.

Keywords: conditional moment equations; controlled stochastic process; first-order Markov process; Cressie-Read power divergence criterion; quadratic loss; adaptive behavior (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2015
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