SNP

Inference for Semi-Nonparametric Econometric Models

Coordinatore	LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	866˙454 €
EC contributo	866˙454 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2013-CoG
Funding Scheme	ERC-CG
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-04-01   -   2019-03-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE  Organization address address: Houghton Street 1 city: LONDON postcode: WC2A 2AE contact info Titolo: Dr. Nome: Taisuke Cognome: Otsu Email: send email Telefono: +44 2079557509 Fax: +44 2079557509	UK (LONDON)	hostInstitution	866˙454.00
2	LONDON SCHOOL OF ECONOMICS AND POLITICAL SCIENCE  Organization address address: Houghton Street 1 city: LONDON postcode: WC2A 2AE contact info Titolo: Mr. Nome: Jonathan Cognome: Deer Email: send email Telefono: +44 20 7106 1202 Fax: +44 20 79556187	UK (LONDON)	hostInstitution	866˙454.00

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 Obiettivo del progetto (Objective)

'This research project aims to contribute to advances in the research on semiparametric and nonparametric econometric methods by developing novel estimation and inference approaches in a variety of contexts and applying them to important empirical problems in economics. The project is divided into four parts. The first part develops a Bayes procedure for the moment condition model. A likelihood function is constructed by employing an information theoretic projection from space of nonparametric prior distributions on data to space of distributions satisfying the moment conditions. Posterior sampling methods and asymptotic theory for the posterior are developed. The second part focuses on the moment condition model but from a different perspective, robustness of decision methods. By focusing on local perturbations within shrinking topological neighborhoods, this part develops a framework to evaluate robustness of inference methods for the moment condition model. It is found that there is a computationally convenient method that achieves optimal minimax robust properties under local perturbations without losing asymptotic equivalence to GMM under correct specification. The third part develops nonparametric regression techniques for extremal or tail behaviors. This part develops asymptotic theory for nonparametric quantile regression when the quantile drifts to 0 or 1 as the sample size increases. Based on the theory, a new inference method for extremal behaviors is developed. The fourth part extends the empirical likelihood approach to a variety of empirical problems in economics. This part develops empirical likelihood inference methods for regression discontinuity designs, continuity or discontinuity of probability density functions, volatility measurement in high frequency financial data, and testing for partially identified moment inequality models. Also new concepts of nonparametric likelihood are developed by extending the likelihood theory on parametric models.'