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Please use this identifier to cite or link to this item: http://hdl.handle.net/2108/341

Title: A dynamic model for binary panel data with unobserved: heterogeneity admitting a root-n consistent conditional estimator
Authors: Bartolucci, Francesco
Nigro, Valentina
Keywords: binary data
quadratic exponential distribution
log-linear models
log-odds ratio
longitudinal data
state dependence
Issue Date: Mar-2007
Publisher: CEIS
Citation: CEIS Tor Vergata - Research Paper Series, Vol. 33, No. 98, March 2007
Series/Report no.: CEIS Tor Vergata Research Paper; 98
Abstract: A model for binary panel data is introduced which allows for state dependence and unobserved heterogeneity beyond the effect of strictly exogenous covariates. The model is of quadratic exponential type and its structure closely resembles that of the dynamic logit model. An economic interpretation of its assumptions, based on expectation about future outcomes, is provided. The main advantage of the proposed model, with respect to the dynamic logit model, is that each individual-specific parameter for the unobserved heterogeneity may be eliminated by conditioning on the sum of the corresponding response variables. A conditional likelihood results which allows us to identify the structural parameters of the model with at least three observations (included an initial observation assumed to be exogenous), even in the presence of time dummies. A root-n consistent conditional estimator of these parameters also results which is very simple to compute. Its finite sample properties are studi...
URI: http://papers.ssrn.com/paper.taf?abstract_id=967389
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