Showing 21 - 30 of 32,654
The second chapter of the dissertation discusses the non-parametric extension of the network formation model in Toth (2018), when the researcher does not assume the functional form of the distance function. An intuitive way for the non-parametric extension is to use the parametric estimator for...
Persistent link: https://www.econbiz.de/10012909992
This paper proposes a new semi-parametric identification and estimation approach to multinomial choice models in a panel data setting with individual fixed effects. Our approach is based on cyclic monotonicity, which is a defining convex-analytic feature of the random utility framework...
Persistent link: https://www.econbiz.de/10012936363
We propose a simple procedure based on an existing “debiased” l_{1}-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number...
Persistent link: https://www.econbiz.de/10012970017
We use data from two representative U.S. household surveys, the Medical Expenditure Panel Survey (MEPS) and the Health and Retirement Study (Rand-HRS) to estimate Markov transition probability matrices between health states over the lifecycle from age 20–95. We use non-parametric and...
Persistent link: https://www.econbiz.de/10013242676
This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied...
Persistent link: https://www.econbiz.de/10011757281
Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non-additive changes in...
Persistent link: https://www.econbiz.de/10012598419
The paper shows that several estimators for the panel probit model suggested in the literature belong to a common class of GMM estimators. They are relatively easy to compute because they are based on conditional moment restrictions involving univariate moments of the binary dependent variable...
Persistent link: https://www.econbiz.de/10010904000
The paper compares two approaches to the estimation of panel probit models: the Generalized Method of Moments (GMM) and the Simulated Maximum Likelihood (SML) technique. Both have in common that they circumvent multiple integrations of joint density functions without the need to impose...
Persistent link: https://www.econbiz.de/10010958307
This paper presents a survey on panel data methods in which Iemphasize new developments. Inparticular, linear multilevel models with a new variant are discussed. Furthermore, non-linear, nonparametric and semiparametric models are analyzed. In contrast to linear models there do not exist unified...
Persistent link: https://www.econbiz.de/10005243316
This paper considers spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects, where the latent dependent variables are spatially correlated. Without imposing any parametric structure of the error terms, this paper proposes a smoothed spatial maximum...
Persistent link: https://www.econbiz.de/10014151984