Artikel
Information recovery in a dynamic statistical Markov model
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 3 ; Year: 2015 ; Issue: 2 ; Pages: 187-198 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Econometric and Statistical Methods: Special Topics: General
Model Construction and Estimation
- Thema
-
conditional moment equations
controlled stochastic process
first-order Markov process
Cressie-Read power divergence criterion
quadratic loss
adaptive behavior
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Miller, Douglas J.
Judge, George
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2015
- DOI
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doi:10.3390/econometrics3020187
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Miller, Douglas J.
- Judge, George
- MDPI
Entstanden
- 2015
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