This paper develops identification and estimation methods for dynamic structural models when agents’ actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model.
We show that the identi fication results of fi nite mixture and misclassifi cation models are equivalent in a widely-used scenario except an extra ordering assumption. In the misclassi fication model, an ordering condition is imposed to pin down the precise values of the latent variable, which are also of researchers' interests and need to be identifi ed.
This paper estimates a structural model of private provision of public goods to provide some new empirical evidence on individuals' strategic contributing behaviors.
This paper reviews recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics.
We suggest an estimator based on non/semi-parametric maximum likelihood, derive its asymptotic properties and illustrate the effectiveness of the method with a simulation study and an application to the relationship between firm investment behaviour and market value, the latter being notoriously mismeasured.
Virtually all methods aimed at correcting for covariate measurement error in regressions rely on some form of additional information (e.g. validation data, known error distributions, repeated measurements or instruments). In contrast, we establish that the fully nonparametric classical errors-in-variables mode is identifiable from data on the regressor and the dependent variable alone, unless the model takes a very specific parametric form.
This paper considers the widely admitted ill-posed inverse problem for measurement error models: estimating the distribution of a latent variable X∗ from an observed sample of X, a contaminated measurement of X∗.
We consider the identification of a Markov process {W<sub>t</sub>, X<sub>t</sub>*} for t=1,2,...,T when only {W<sub>t</sub>} for t=1, 2,..,T is observed.
This note considers nonparametric identification of a general nonlinear regression model with a dichotomous regressor subject to misclassification error.
We consider the identification of a Markov process {W<sub>t</sub>, X<sub>t</sub>*} for t=1,2,...,T when only {W<sub>t</sub>} for t=1, 2,..,T is observed.
