Nonparametric identification of dynamic models with unobserved state variables
We consider the identification of a Markov process {Wt,Xt∗} when only {Wt} is observed. In structural dynamic models, Wt includes the choice variables and observed state variables of an optimizing agent, while Xt∗ denotes time-varying serially correlated unobserved state variables (or agent-specific unobserved heterogeneity). In the non-stationary case, we show that the Markov law of motion fWt,Xt∗∣Wt−1,Xt−1∗ is identified from five periods of data Wt+1,Wt,Wt−1,Wt−2,Wt−3. In the stationary case, only four observations Wt+1,Wt,Wt−1,Wt−2 are required. Identification of fWt,Xt∗∣Wt−1,Xt−1∗ is a crucial input in methodologies for estimating Markovian dynamic models based on the “conditional-choice-probability (CCP)” approach pioneered by Hotz and Miller.
Year of publication: |
2012
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Authors: | Hu, Yingyao ; Shum, Matthew |
Published in: |
Journal of Econometrics. - Elsevier, ISSN 0304-4076. - Vol. 171.2012, 1, p. 32-44
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Publisher: |
Elsevier |
Saved in:
Online Resource
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