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 / A.G. Billé, S. Leorato. - In: ECONOMETRIC REVIEWS. - ISSN 0747-4938. - (2019). [Epub ahead of print]

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

S. Leorato
Ultimo
2019

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.
Spatial autoregressive–regressive probit model, nonlinear modeling, SARAR, partial maximum likelihood, Marginal effects
Settore SECS-S/01 - Statistica
Settore SECS-P/05 - Econometria
2019
nov-2019
Article (author)
File in questo prodotto:
File Dimensione Formato  
QML-spatial-probit_v8.pdf

Open Access dal 02/11/2020

Tipologia: Pre-print (manoscritto inviato all'editore)
Dimensione 780.57 kB
Formato Adobe PDF
780.57 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

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/2434/689495
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact