NONLINEAR PANEL

"Nonlinear Panel Data Models: Heterogeneity, Identification and Estimation."

Coordinatore	UNIVERSIDAD CARLOS III DE MADRID    more  Organization address address: CALLE MADRID 126 city: GETAFE (MADRID) postcode: 28903 contact info Titolo: Dr. Nome: Raquel Cognome: Carrasco Email: send email Telefono: +34 916249583 Fax: +34 916249329
Nazionalità Coordinatore	Spain [ES]
Totale costo	151˙082 €
EC contributo	151˙082 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2007-4-1-IOF
Funding Scheme	MC-IOF
Anno di inizio	2008
Periodo (anno-mese-giorno)	2008-08-01   -   2010-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSIDAD CARLOS III DE MADRID  Organization address address: CALLE MADRID 126 city: GETAFE (MADRID) postcode: 28903 contact info Titolo: Dr. Nome: Raquel Cognome: Carrasco Email: send email Telefono: +34 916249583 Fax: +34 916249329	ES (GETAFE (MADRID))	coordinator	0.00

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 Obiettivo del progetto (Objective)

'Unobserved heterogeneity is an important factor to take into account when making inference based on micro-data. In linear panel data models it is well known how to control for permanent unobserved heterogeneity in a robust way, i.e. without assuming any parametric distribution of the heterogeneity on the population. In contrast the problem is much more difficult in nonlinear models. A significant part of the research on microeconometrics in the recent years has been about dealing with this issue and many solutions have been proposed. A first objective of this proposal is to study how well the bias correction methods recently proposed work for other specific nonlinear models and data set of interest in applied econometrics. This will provide practitioners with reliable evidence about whether the methods work for the case they are interested in and which method is better among the many that have been proposed. Unobserved heterogeneity in dynamic discrete choice models is usually only allowed through a specific constant individual term, as in linear panel data models. However, in nonlinear cases that assumption implies further strong restrictions in the model and previous explorations seems to indicate that we should allow for more heterogeneity. A second objective of this proposal is to analyze identification of a dynamic discrete choice panel model where not only the intercept but also the slope is heterogeneous. This will include to (i) see how much restrictions we have to impose on the distribution to point identify the model (or its parameters of interest) from a cross-section of a fixed periods; and (ii) study further the non-identified situations of that model by looking for partial identification. The interest on this is to know how much heterogeneity can be identify from a given data set, the minimum restrictions we have imposed to get point estimates of the parameters of interests, and what we still can learn if we do not imposed all those restrictions.'