In this paper, we propose a Partial MLE (PMLE) for a general spatial nonlinear probit model, i.e., SARAR(1,1) probit, defined through a SARAR(1,1) latent linear model. This model encompasses both the SAE(1) probit and the more interesting SAR(1) probit models, already considered in the literature. We provide a complete asymptotic analysis of our PMLE as well as appropriate definitions of the marginal effects. Moreover, we address the issue of the choice of the groups (couples, in our case) by proposing an algorithm based on a minimum KL divergence problem. Finite sample properties of the PMLE are studied through extensive Monte Carlo simulations. In particular, we consider both sparse and dense matrices for the true spatial model specifications, and cases of model misspecification given wrong assumed weighting matrices. In a real data example, we finally also compare our estimator with different MLE–based estimators and with the Bayesian approach.

Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances / Anna Gloria Billé; Samantha Leorato. - In: ECONOMETRIC REVIEWS. - ISSN 0747-4938. - STAMPA. - 39:5(2020), pp. 437-475. [10.1080/07474938.2019.1682314]

Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances

Anna Gloria Billé
Primo
;
2020

Abstract

In this paper, we propose a Partial MLE (PMLE) for a general spatial nonlinear probit model, i.e., SARAR(1,1) probit, defined through a SARAR(1,1) latent linear model. This model encompasses both the SAE(1) probit and the more interesting SAR(1) probit models, already considered in the literature. We provide a complete asymptotic analysis of our PMLE as well as appropriate definitions of the marginal effects. Moreover, we address the issue of the choice of the groups (couples, in our case) by proposing an algorithm based on a minimum KL divergence problem. Finite sample properties of the PMLE are studied through extensive Monte Carlo simulations. In particular, we consider both sparse and dense matrices for the true spatial model specifications, and cases of model misspecification given wrong assumed weighting matrices. In a real data example, we finally also compare our estimator with different MLE–based estimators and with the Bayesian approach.
2020
Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances / Anna Gloria Billé; Samantha Leorato. - In: ECONOMETRIC REVIEWS. - ISSN 0747-4938. - STAMPA. - 39:5(2020), pp. 437-475. [10.1080/07474938.2019.1682314]
Anna Gloria Billé; Samantha Leorato
File in questo prodotto:
File Dimensione Formato  
Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances.pdf

accesso riservato

Tipo: Versione (PDF) editoriale
Licenza: Licenza per accesso riservato
Dimensione 3.27 MB
Formato Adobe PDF
3.27 MB Adobe PDF   Visualizza/Apri   Contatta l'autore
QML-spatial-probit_v8.pdf

Open Access dal 01/12/2020

Descrizione: AAM
Tipo: Postprint
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale (CCBYNC)
Dimensione 755.4 kB
Formato Adobe PDF
755.4 kB Adobe PDF Visualizza/Apri

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/11585/853789
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 3
social impact