Information recovery in a dynamic statistical Markov model

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 2 ; Pages: 187-198 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods: Special Topics: General ; Model Construction and Estimation
• Subject: conditional moment equations ; controlled stochastic process ; first-order Markov process ; Cressie-Read power divergence criterion ; quadratic loss ; adaptive behavior

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Miller, Douglas J. ; Judge, George
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2015

Further information

• DOI: doi:10.3390/econometrics3020187
• Handle: http://hdl.handle.net/10419/171822
• Last update: 10.03.2025, 11:44 AM CET