Alternative computational approaches to inference in the multinomial probit model
By John Geweke, Michael P. Keane, D. Runkle. 1994
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Citations in the corpus
- Individual characteristics and stated preferences for alternative energy sources and propulsion technologies in vehicles (2012, unknown): “…if N Nlim = 0 R→∞ Electronic copy available at: https://ssrn.com/abstract=1585142 12 The small sample properties of the simulated maximum likelihood estimation, incorporating the GHK simulator, of multinomial probit models have already been investigated in the past (e.g. Geweke et al., 1994, 1997).”
- Smoothly mixing regressions (2007, methodology): “This is an advantage of Bayesian MCMC approaches generally over methods like simulated maximum likelihood and simulated method of moments that require the computation of state probabilities (see Geweke et al. (1994) and Geweke and Keane (2001)).”
- Fuel choices in urban Indian households (2005, unknown): “In order to explore if such an assumption is realistic, we also considered two non-ordered discrete choice models, namely a multinomial probit model in line with Geweke et al. (1994) and a multinomial logit specification.”
- The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity (2010, result): “Biases of this magnitude are not surprising, in light of prior work showing it is often difficult to pin down error variance-covariance parameters in discrete-choice models (e.g., Geweke et al. 1994).”
- Multinomial probit and multinomial logit: a comparison of choice models for voting research (2004, methodology): “We read these studies, as well as related studies by Horowitz (1980), Keane (1992) and Geweke et al. (1994), as providing little evidence that there are significant, tangible, gains from the MNP specification in typical political science application.8 Transportation engineers might gain increases…”