Information Recovery in a Dynamic Statistical Markov Model

17 Pages Posted: 2 Dec 2012

See all articles by Douglas J. Miller

Douglas J. Miller

University of Missouri at Columbia - Department of Economics

George Judge

University of California, Berkeley - Department of Agricultural & Resource Economics

Date Written: November 12, 2012

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 like 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. Given a model of the 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 sampling experiments are used to illustrate the finite sample performance.

Keywords: conditional moment equations, controlled stochastic process, first-order Markov process, Cressie-Read power divergence criterion, quadratic loss

JEL Classification: C40, C51

Suggested Citation

Miller, Douglas J. and Judge, George G., Information Recovery in a Dynamic Statistical Markov Model (November 12, 2012). Available at SSRN: https://ssrn.com/abstract=2183398 or http://dx.doi.org/10.2139/ssrn.2183398

Douglas J. Miller

University of Missouri at Columbia - Department of Economics ( email )

118 Professional Building
Columbia, MO 65211
United States

George G. Judge (Contact Author)

University of California, Berkeley - Department of Agricultural & Resource Economics ( email )

207 Giannini Hall
University of California
Berkeley, CA 94720
United States

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