23,263 research outputs found
Identification of structural dynamic discrete choice models
This paper presents new identification results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the distribution of unobserved heterogeneities is nonparametric. The proposed identification method does not rely on the availability of terminal period data and hence can be applied to infinite horizon structural dynamic models. For identification we assume availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identified using the control function approach. This is a substantial revision of "Semiparametric identification of structural dynamic optimal stopping time models", CWP06/07.
Semiparametric identification of structural dynamic optimal stopping time models
This paper presents new identification results for the class of structural dynamic optimal stopping time models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where the utility function of an absorbing choice in the model is parametric but the distribution of unobserved heterogeneity is nonparametric. Our identification strategy depends on availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the dynamic optimal stopping model is semiparametrically identified using control function approaches.Structural dynamic discrete choice models, semiparametric identification, optimal stopping
Maximum Score Estimation of Preference Parameters for a Binary Choice Model under Uncertainty
This paper develops maximum score estimation of preference parameters in the
binary choice model under uncertainty in which the decision rule is affected by
conditional expectations. The preference parameters are estimated in two
stages: we estimate conditional expectations nonparametrically in the first
stage and then the preference parameters in the second stage based on Manski
(1975, 1985)'s maximum score estimator using the choice data and first stage
estimates. The paper establishes consistency and derives rate of convergence of
the two-stage maximum score estimator. Moreover, the paper also provides
sufficient conditions under which the two-stage estimator is asymptotically
equivalent in distribution to the corresponding single-stage estimator that
assumes the first stage input is known. These results are of independent
interest for maximum score estimation with nonparametrically generated
regressors. The paper also presents some Monte Carlo simulation results for
finite-sample behavior of the two-stage estimator
Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth About Happiness Scales
Econometric analyses in the happiness literature typically use subjective
well-being (SWB) data to compare the mean of observed or latent happiness
across samples. Recent critiques show that comparing the mean of ordinal data
is only valid under strong assumptions that are usually rejected by SWB data.
This leads to an open question whether much of the empirical studies in the
economics of happiness literature have been futile. In order to salvage some of
the prior results and avoid future issues, we suggest regression analysis of
SWB (and other ordinal data) should focus on the median rather than the mean.
Median comparisons using parametric models such as the ordered probit and logit
can be readily carried out using familiar statistical softwares like STATA. We
also show a previously assumed impractical task of estimating a semiparametric
median ordered-response model is also possible by using a novel constrained
mixed integer optimization technique. We use GSS data to show the famous
Easterlin Paradox from the happiness literature holds for the US independent of
any parametric assumption
Entanglement Detection by Local Orthogonal Observables
We propose a family of entanglement witnesses and corresponding positive maps
that are not completely positive based on local orthogonal observables. As
applications the entanglement witness of the bound entangled state
[P. Horodecki, Phys. Lett. A {\bf 232}, 333 (1997)] is explicitly constructed
and a family of -dimensional bound entangled states is designed so that the
entanglement can be detected by permuting local orthogonal observables. Further
the proposed physically not implementable positive maps can be physically
realized by measuring a Hermitian correlation matrix of local orthogonal
observables.Comment: 4 pages, 1 figur
- …