Probit in a Spatial Context: A Monte Carlo Analysis

By Kurt J. Beron, Wim P. M. Vijverberg. 2004

167 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/6f6b2bac3d31a57db19af4191b035855d9d033ea

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In Economics and Econometrics

1. Alfonso Flores-Lagunes
2. Y. Nakano
3. Takeshi Aida
4. Valerien Pede
5. Takuji W. Tsusaka
6. Chandra R. Bhat
7. K. Schnier
8. Kenneth Train

Citations in the corpus

• Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study (2013, unknown): “h parameter, and thus compute different moments of the distribution. The second advantage is its flexibility to account for the heteroskedasticity in the error terms. 3.3 Recursive Importance Sampling Beron and Vijverberg (2004) propose a recursive importance sampling (RIS) estimator to evaluate dire”
• The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, unknown): “…models are the frequentist recursive importance sampling (RIS) estimator (which is a generalization of the more familiar Geweke-Hajivassiliou-Keane or GHK simulator; see Beron and Vijverberg, 2004) and the Bayesian Markov Chain Monte Carlo (MCMC)-based estimator (see LeSage and Pace, 2009).”
• The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, methodology): “In fact, as demonstrated by Varin and Vidoni (2009), Varin and Czado (2010), Bhat et al. (2010a), and Apanasovich et al.”
• Estimation of Sample Selection Models with Spatial Dependence (2012, methodology): “This literature has concentrated mainly on the probit model with spatial effects, as in Beron and Vijverberg (2004), Case (1992), Fleming (2004), LeSage (2000), McMillen (1992), and Pinkse and Slade (1998).”
• Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania (2018, background): “9 in Beron and Vijverberg (2004), spatial LPM yields conservative estimates of q and k as compared to spatial Probit estimators.”
• A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, unknown): “…frequentist recursive importance sampling (RIS) estimator (which is a generalization of the more familiar GewekeHajivassiliou-Keane or GHK simulator; see Beron et al., 2003 and Beron and Vijverberg, 2004) and the Bayesian Markov Chain Monte Carlo (MCMC)-based estimator (see LeSage and Pace, 2009).”