A COMPUTATIONALLY EFFICIENT QUADRATURE PROCEDURE FOR THE ONE-FACTOR MULTINOMIAL PROBIT MODEL

By J. S. Butler, Robert A. Moffitt. 1982

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Citations in the corpus

Panel Data Discrete Choice Models of Consumer Demand (2013, methodology): “Butler and Moffitt (1982) describe the derivation of the weights and points, and find that rather accurate evaluations of normal integrals can be obtained using just several points (i.e., typically only 6 or 7).”
An analysis of the determinants of job satisfaction when individuals’ baseline satisfaction levels may differ (2007, methodology): “We analyse overall job satisfaction by means of the random effects ordered probit model (Butler and Moffitt, 1982) and the fixed effects ordered logit estimator recently proposed by Ferrer-i-Carbonel and Frijters (2004).5 For comparison purposes and as a robustness check we also use the estimator…”
Simplified Implementation of the Heckman Estimator of the Dynamic Probit Model and a Comparison with Alternative Estimators (2009, unknown): “In the standard case considered here, α is taken to be normally distributed and the integral in (3) can be evaluated using Gaussian--Hermite quadrature (Butler and Moffitt, 1982).”
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, methodology): “For two-level random component (also called random intercept) binary probit models, this approach is often attributed to Butler and Moffitt (1982) although it was introduced earlier for closely related models by Bock and Lieberman (1970).”
Ordering effects and choice set awareness in repeat-response stated preference studies (2012, methodology): “can be calculated using Gaussian quadrature [15] while simulation methods are used to approximate the inner integral over si.”
Discrete Choice Modeling (2007, methodology): “In the simplest case, the quadrature method of Butler and Moffitt (1982) can be used to maximize the log likelihood.”
Drug Dealing and Legitimate Self‐Employment (2002, methodology): “18 See Butler and Moffitt (1982) for more details on the following derivations.”
Are Economists More Likely to Hold Stocks (2007, unknown): “Focusing on the educational indicators, their effects are identified 11Given that we only have two choices and five time periods, this is the most efficient procedure for integrating out ci, cf. Butler & Moffitt (1982).”
Toward an Understanding of the Economics of Charity: Evidence from a Field Experiment (2006, unknown): “As a robustness check, we apply the two-step selection model of Butler and Moffitt’s (1982)”
Market structure and entry: Where’s the beef? (2001, methodology): “14 We employ the standard quadrature method (with 20 evaluation points) suggested by Butler and Moffit (1982) to estimate the random-effects probit.”
What Do Social Scientists Know About the Benefits of Marriage? A Review of Quantitative Methodologies (2004, unknown): “If µi and θi are assumed to be normally distributed, longitudinal equations (5) and (3’) can be jointly estimated using an extension of the random effects procedure described by Butler and Moffitt (1982).”
Works Councils and Environmental Investment: Theory and Evidence from German Panel Data (2006, unknown): “With the dependent variables as a categorical ('no', 'yes'), and firm-specific characteristics likely to play a significant role in investment decisions, we have used a random-effects-probit model (Butler and Moffitt 1982).”
In search of complementary assets: The determinants of alliance formation of high-tech start-ups (2006, background): “19 See Bulter and Moffitt (1982) and Greene (2000) for full details on”