一步还是两步?空间二元probit模型的GMM效率评估

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2023-09-01 DOI:10.1016/j.jocm.2023.100432
Gianfranco Piras , Mauricio Sarrias
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引用次数: 0

摘要

在本文中,我们提出了一个空间二进制Probit模型的两步广义矩方法(GMM)过程。特别地,我们提出了一系列基于第一步中矩条件的加权矩阵的不同选择的两步估计量,以及估计系数的方差-协方差矩阵的不同估计量。在蒙特卡洛实验的背景下,我们比较了这些估计量的性质,一步GMM的线性化版本和递归重要性采样器(RIS)。我们的研究结果表明,在力矩条件下选择权重矩阵和采用两步程序都有好处。
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One or two-step? Evaluating GMM efficiency for spatial binary probit models

In this article we propose two-step generalized method of moment (GMM) procedure for a Spatial Binary Probit Model. In particular, we propose a series of two-step estimators based on different choices of the weighting matrix for the moments conditions in the first step, and different estimators for the variance–covariance matrix of the estimated coefficients. In the context of a Monte Carlo experiment, we compare the properties of these estimators, a linearized version of the one-step GMM and the recursive importance sampler (RIS). Our findings reveal that there are benefits related both to the choice of the weight matrix for the moment conditions and in adopting a two-step procedure.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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