A CONDITIONAL PROBIT MODEL FOR QUALITATIVE CHOICE: DISCRETE DECISIONS RECOGNIZING INTERDEPENDENCE AND HETEROGENEOUS PREFERENCES'

By Jerry A. Hausman, D. Wise. 1978

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Why Do States Adopt Renewable Portfolio Standards?: An Empirical Investigation (2010, background): “The multinomial logit model is computationally cheaper but relies on the assumption of the independence of irrelevant alternatives (IIA) (Hausman and Wise 1978, Wooldridge 2002).”
On the Empirical Content of Quantal Response Equilibrium (2008, unknown): “, McFadden (1978), Cardell (1997)) or random coefficients probit/logit (e.”
Issues, Economics, and the Dynamics of Multiparty Elections: The British 1987 General Election (2000, methodology): “When we impose a speci c set of assumptions about the distribution of therandom component (here that they have a multivariate normal distribution) we obtain a speci crandom utility model (here MNP).4The MNP model was developed by Hausman and Wise (1978).”
Measuring Market Power in the Ready-to-Eat Cereal Industry (2001, unknown): “In particular, the multinomial Probit model (see Hausman and Wise, 1978) and the "universal" Logit (see McFadden, 1981).”
Restrictions of economic theory in nonparametric methods (1994, background): “, eJ) depend on (s,z) is important because there is evidence that the estimators for discrete choice models may be very sensitive to heteroskedasticity of E (Hausman and Wise (1978)).”
Modelling Recreation Demand Using Choice Experiments: Climbing in Scotland (2002, methodology): “These include the multinomial probit (Hausman and Wise, 1978), the nested logit (Wiseman and Koppleman, 1993), the random parameters logit model (Train, 1998) and the heterogeneous extreme value logit (Bhat, 1995; Allenby and Ginter, 1995).”
Cost-Benefit Analysis and the Environment: Recent Developments (2006, unknown): “These include the multinomial probit (Hausman and Wise, 1978), the nested logit (McFadden, 1981) and the random ∑∑ ∑= = = = N i J j J j ij ij ij V VyL 1 1 1 ] )exp( )exp( log[log ⎪ ⎭ ⎪ ⎬ ⎫ ⎪⎩ ⎪ ⎨ ⎧ = ∑ ∑ − i i i i y V V bWTP )exp( )exp( ln 0 1 1 y C b bWTP −= COST-BENEFIT ANALYSIS AND THE…”
Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences (2003, result): “This formulation results in the traditional multinomial probit model (see Hausman and Wise, 1978).”
Investigating Category Pricing Behavior at a Retail Chain (2002, background): “For early examples of such analyses, see Hausman and Wise (1978) and Beggs, Cardell, and Hausman (1981), and for a marketing application, see Kamakura and Russell (1989).”
Multilevel logistic regression for polytomous data and rankings (2003, methodology): “A probit version of this kind of model was considered by Hausman and Wise (1978). 2.”