Journal ArticleDOI
The method of simulated scores for the estimation of LDV models
TLDR
The Simulated Score (MSS) estimator as discussed by the authors uses a recursive conditioning of the multivariate normal density through a Cholesky triangularization of its variance-covariance matrix.Abstract:Â
The method of simulated scores (MSS) is presented for estimating limited dependent variables models (LDV) with flexible correlation structure in the unobservables. We propose simulators that are continuous in the unknown parameter vectors, and hence standard optimization methods can be used to compute the MSS estimators that employ these simulators. The first continuous method relies on a recursive conditioning of the multivariate normal density through a Cholesky triangularization of its variance-covariance matrix. The second method combines results about the conditionals of the multivariate normal distribution with Gibbs resampling techniques. We establish consistency and asymptotic normality of the MSS estimators and derive suitable rates at which the number of simulations must rise if biased simulators are used.read more
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Book
Discrete Choice Methods with Simulation
TL;DR: In this paper, the authors describe the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation, and compare simulation-assisted estimation procedures, including maximum simulated likelihood, method of simulated moments, and methods of simulated scores.
Journal ArticleDOI
Mixed mnl models for discrete response
Daniel McFadden,Kenneth Train +1 more
TL;DR: In this article, the adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately definedartificial variables, and a practicalestimation of aarametricmixingfamily can be run by MaximumSimulated Likelihood EstimationorMethod ofSimulatedMoments, andeasilycomputedinstruments are provided that make the latter procedure fairly eAcient.
Journal ArticleDOI
Fitting Fully Observed Recursive Mixed-process Models with cmp:
TL;DR: In this paper, the authors present a probit, ordered probit model and a multinomial pro... model for estimating a linear function and a normal error in a small-sample linear regression model.
Journal ArticleDOI
Forecasting new product penetration with flexible substitution patterns
David Brownstone,Kenneth Train +1 more
TL;DR: The authors describe and apply choice models, including generalizations of logit called mixed logits, that do not exhibit the restrictive "independence from irrelevant alternatives" property and can approximate any substitution pattern.
Journal ArticleDOI
Estimating fully observed recursive mixed-process models with cmp 1
TL;DR: The Stata module CMP as mentioned in this paper fits Seemingly Unrelated Regressions (SUR) models of this broad family and can mimic a dozen built-in Stata commands and several user-written ones.
References
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Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Journal ArticleDOI
Sampling-Based Approaches to Calculating Marginal Densities
TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.