On the Use of Simulated Frequencies to Approximate Choice Probabilities

By Steven R. Lerman, Charles F. Manski. 1981

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Citations in the corpus

Panel Data Discrete Choice Models of Consumer Demand (2013, methodology): “As Lerman and Manski (1981) discussed, very large simulations sizes are needed to provide accurate simulated probabilities of low probability events.”
Modelling income processes with lots of heterogeneity (2010, methodology): “Ultimately all of these methods take their inspiration from Lerman and Manski (1981) and the important nding that we can consistently estimate using simulation methods with a xed number of simulations, (see McFadden (1989) and Pakes and Pollard (1989)).”
A Dynamic Oligopoly Structural Model for the Prescription Drug Market After Patent Expiration (2010, unknown): “In the simulation approach, one uses Monte Carlo methods to simulate the high order integrals that enter the likelihood function rather than evaluating them numerically (Pakes[43], Lerman and Manski[37], McFadden[40], Pakes and Pollard[46], Keane[35]).”
Discrete Choice Methods with Simulation by Kenneth E. Train (2003, methodology): “It was originally proposed for probits (Manski and Lerman, 1981), and we give the details of the approach in terms of the probit model.”
Discrete Choice Methods with Simulation (2016, methodology): “It was originally proposed for probits (Manski and Lerman, 1981), and we give the details of the approach in terms of the probit model.”
The Effect of Parental Transfers and Borrowing Constraints On Educational Attainment (1999, unknown): “As we describe below, an external validation of the predictions from the estimates of our model is provided by Leslie (1984), who found that the when government sponsored grants and guaranteed student loans were made more generous in the 1970s, earnings of college students did in fact decline substa”
Extended discrete choice models : integrated framework, flexible error structures, and latent variables (2001, methodology): “Much of the research on probit is in the areas of estimation (for example , Clark, 1961, developed an early used approximation; Lerman and Manski, 1981, pioneered the use of simulation for econometric models; and Geweke, Hajivassiliou, and Keane developed the now common GHK simulator4, which made…”
Consumer Learning and Heterogeneity: Dynamics of Demand for Prescription Drugs after Patent Expiration (2010, unknown): “APPENDIX In the simulation approach, one uses Monte Carlo methods to simulate the high order integrals that enter the likelihood function rather than evaluating them numerically (Pakes[45], Lerman and Manski[34], McFadden[40], Pakes and Pollard[46], Keane[32]).”
Alternative sampling methods for estimating multivariate normal probabilities (2003, methodology): “MatouR sek (1998) and Hong and Hickernell (2003) show that random linear scrambling yields randomized (t; m; s)-nets with the equidistribution property of the original nets and contain points that are uniformly distributed.”
Approximate Gaussian Integration using Expectation Propagation (2011, methodology): “There are special cases where analytic decompositions (Plack-ett, 1954; Huguenin et al., 2009) or sampling meth-ods (Lerman and Manski, 1981; Pakes and Pollard, 1989) are possible though very costly, but the most eﬃcient general, known method is numerical integration (the recent book Genz and Bretz…”