@techreport{Hu2008Nonparametric,
abstract = {We consider the identification of a Markov process {Wt,Xt*} for t = 1, 2, ... , T when only {Wt} for t = 1, 2, ... , T is observed. In structural dynamic models, Wt denotes the sequence of choice variables and observed state variables of an optimizing agent, while Xt* denotes the sequence of serially correlated unobserved state variables. The Markov setting allows the distribution of the unobserved state variable Xt* to depend on Wt-1 and Xt-1*. We show that the joint distribution f Wt, Xt* | Wt-1, Xt-1* is identified from the observed distribution f Wt+1, Wt | Wt-1, Wt-2, Wt-3 under reasonable assumptions. Identification of f Wt, Xt*, Wt-1, Xt-1* is a crucial input in methodologies for estimating dynamic models based on the conditional-choice-probability (CCP) approach pioneered by Hotz and Miller.},
address = {Baltimore, Md.},
author = {Yingyao Hu and Matthew Shum},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330; Markovscher Prozess; \"{O}konometrie},
language = {eng},
number = {543},
publisher = {Johns Hopkins Univ., Dep. of Economics},
title = {Nonparametric identification of dynamic models with unobserved state variables},
type = {Working papers // the Johns Hopkins University, Department of Economics},
url = {http://hdl.handle.net/10419/49893},
year = {2008}
}
