In this paper we study a family of linear regression models with spatial dependence in the errors and in the dependent variable. The spatial dependence is modeled by arbitrary matrix functions Vn and Mn respectively, indexed by a scalar parameter and, eventually, by two (possibly distinct) weight matrices, D and W. We define the quasi maximum likelihood estimator and study its asymptotic properties under non-Gaussian errors. We use the results on the general model to define a wide class of spatial models, defined as matrix transformations of a given weight matrix. This family is large enough to encompass some popular models used in the spatial econometric literature, such as SARAR and MESS models. By defining broad families of models, where matrix transformations are associated to distribution functions, we provide some insights into the difference between specifications, with emphasis on advantages and shortcomings as well as on interpretation of the parameters and correspondences between models.

Matrix Spatial Specification models

Martinelli, Andrea
2020

Abstract

In this paper we study a family of linear regression models with spatial dependence in the errors and in the dependent variable. The spatial dependence is modeled by arbitrary matrix functions Vn and Mn respectively, indexed by a scalar parameter and, eventually, by two (possibly distinct) weight matrices, D and W. We define the quasi maximum likelihood estimator and study its asymptotic properties under non-Gaussian errors. We use the results on the general model to define a wide class of spatial models, defined as matrix transformations of a given weight matrix. This family is large enough to encompass some popular models used in the spatial econometric literature, such as SARAR and MESS models. By defining broad families of models, where matrix transformations are associated to distribution functions, we provide some insights into the difference between specifications, with emphasis on advantages and shortcomings as well as on interpretation of the parameters and correspondences between models.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2114053
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact