Abstract: - Information theoretic estimators do not require specification of the specific parametric functional form of sampling distributions or likelihood functions but it is only necessary to make mild assumptions concerning the existence of zero-valued moment conditions. This work aims at developing an entropy-based estimation strategy of a dynamic spatial heteroge-neous panel data model where separate processes for each unit are considered. An empirical application is provided to demonstrate practical implementation of the GME estimator when one has to deal with estimation of ill-posed or ill-conditioned models in analyzing spatial structures. Keywords: - Dynamic panel data, Generalized maximum entropy estimation, Spatial models

Esimating mixed spatial processes in information-theoretic frameworks

BERNARDINI PAPALIA, ROSA
2009

Abstract

Abstract: - Information theoretic estimators do not require specification of the specific parametric functional form of sampling distributions or likelihood functions but it is only necessary to make mild assumptions concerning the existence of zero-valued moment conditions. This work aims at developing an entropy-based estimation strategy of a dynamic spatial heteroge-neous panel data model where separate processes for each unit are considered. An empirical application is provided to demonstrate practical implementation of the GME estimator when one has to deal with estimation of ill-posed or ill-conditioned models in analyzing spatial structures. Keywords: - Dynamic panel data, Generalized maximum entropy estimation, Spatial models
Recent advances in mathematics and computers in business and economics
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242
Bernardini Papalia R.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/76978
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