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.
Highlights
In this paper we recognize that understanding and predicting the future state of an economic-behavioral process is statistical in nature and that the underlying dynamics may be complex and not well understood
This permits us to exploit the statistical machinery of information theory to gain insights into the unknown transition parameters and the underlying probability distribution behavior of the dynamic state space process
In terms of estimator choice, we use in a statistical loss function context, a convex combination of entropy functionals-likelihoods from the Cressie-Read (CR) family
Summary
Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel. Suggested Citation: Miller, Douglas J.; Judge, George (2015) : Information recovery in a dynamic statistical Markov model, Econometrics, ISSN 2225-1146, MDPI, Basel, Vol 3, Iss. 2, pp. Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen. Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Graduate School, 207 Giannini Hall, University of California, Berkeley, Berkeley, CA 94720, USA. Received: 17 October 2014 / Accepted: 25 February 2015 / Published: 25 March 2015
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