Comparison method of spatial autoregressive probit estimation

Fevi Novkaniza, Anik Djuraidah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Probit models with spatial dependencies were first studied by McMillen (1992), where an EM Algorithm was developed to produce consistent (maximum likelihood) estimates for these models. In spatial autoregressive probit model, the spatial dependent structure adds complexity in the estimation of parameters. LeSage and Smith (2001) use Bayesian estimation via Markov Chain Monte Carlo methods that sample sequentially from the complete set of conditional distributions for all parameters. Klier and McMillen (2008) have proposed a linearized version of the GMM estimator that avoids the infeasible problem of inverting n-by-n matrices when employing large samples. They show that standard GMM reduces to a nonlinear two-stage least squares problem. Martinetti and Geniaux (2017) proposed approximate likelihood estimation which based on the full maximization of likelihood of an approximate multivariate normal distribution function. We use some extensive simulation for these methods and show the best estimation method which can handle sample sizes with many observations and various value of coefficient spatial lag, provided the spatial weight matrix is inconvenient sparse form, as is for large data sets, where each observation neighbours only a few other observations.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Industrial Engineering and Operations Management, IEOM 2018
PublisherIEOM Society
Pages2016-2017
Number of pages2
ISBN (Print)9781532359446
Publication statusPublished - 1 Jan 2018
Event8th International Conference on Industrial Engineering and Operations Management, IEOM 2018 - Bandung, Indonesia
Duration: 6 Mar 20188 Mar 2018

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
Volume2018-March
ISSN (Electronic)2169-8767

Conference

Conference8th International Conference on Industrial Engineering and Operations Management, IEOM 2018
CountryIndonesia
CityBandung
Period6/03/188/03/18

Keywords

  • Approximate likelihood
  • Bayes
  • Linearized GMM
  • Probit
  • SAR

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