We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is...

Estimation results obtained by parametric models may be seriously misleading when the model is misspecified or poorly approximates the true model. This study proposes a test that jointly tests the specifications of multiple response probabilities in unordered multinomial choice models. The test...

Nonparametric regression is often considered as unreliable in small samples when the number of regressors is large. This may be different if the dependent variable is bounded. First, the small-sample properties of nonparametric regression with 14 regressors (4 continuous, 10 binary) are...

Motivated by the promising performance of alternative estimation methods for mixed logit models, in this paper we derive, implement, and test expectation-maximization (EM) and minorization-maximization (MM) algorithms to estimate the semiparametric logit mixed logit (LML) and mixture-of-normals...

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component...

The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time...

This paper introduces the conditional likelihood estimator of relative risk (CLERR). The CLERR estimates the relative risk of an outcome analogously to the way the conditional logit estimates an odds ratio. Aside from the fact that relative risk is often the preferred measure of association, the...

We address two issues in nonparametric structural analyses of dynamic binary choice processes (DBCP). First, the DBCP is not testable and decision makers’ single-period payoffs (SPP) cannot be identified even when the distribution of unobservable states (USV) is known. Numerical examples show...

We present a computationally tractable method for simulating arbitrage free implied volatility surfaces. We illustrate how our method may be combined with a factor model for the implied volatility surface to generate dynamic scenarios for arbitrage-free implied volatility surfaces. Our approach...

We introduce a nonparametric block bootstrap approach for Quasi-Likelihood Ratio type tests of nonlinear restrictions. Our method applies to extremum estimators, such as quasi-maximum likelihood and generalized method of moments estimators. Unlike existing parametric bootstrap procedures for...