In statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married. Theâ€¦Â (More)

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Highly Cited

2010

Highly Cited

2010

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

Abstract. This paper explains why computing the marginal effect of a change in two variables is more complicated in nonlinearâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regressionâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

1. Page 86 as part of the discussion of the robust option, logit and probit were switched. It should read: " For example, if theâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Mark A. Girolami, Simon Rogers
- Neural Computation
- 2006

It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latentâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of groupâ€¦Â (More)

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Highly Cited

2003

Highly Cited

2003

- John Geweke, Michael P. Keane, David E. Runk
- 2003

Statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numericalâ€¦Â (More)

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2002

2002

A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are oftenâ€¦Â (More)

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Highly Cited

1994

Highly Cited

1994

We develop new methods for conducting a finite sample, likelihood-based analysis of the multinomial probit model. Using a variantâ€¦Â (More)

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1991

1991

Random utility models often involve terms which represent alternative-specific errors, and the main attractive feature of theâ€¦Â (More)

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