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This book describes the new generation of discrete choice methods,
focusing on the many advances that are made possible by simulation.
Researchers use these statistical methods to examine the choices
that consumers, households, firms, and other agents make. Each of
the major models is covered: logit, generalized extreme value, or
GEV (including nested and cross-nested logits), probit, and mixed
logit, plus a variety of specifications that build on these basics.
Simulation-assisted estimation procedures are investigated and
compared, including maximum stimulated likelihood, method of
simulated moments, and method of simulated scores. Procedures for
drawing from densities are described, including variance reduction
techniques such as anithetics and Halton draws. Recent advances in
Bayesian procedures are explored, including the use of the
Metropolis-Hastings algorithm and its variant Gibbs sampling. The
second edition adds chapters on endogeneity and
expectation-maximization (EM) algorithms. No other book
incorporates all these fields, which have arisen in the past 25
years. The procedures are applicable in many fields, including
energy, transportation, environmental studies, health, labor, and
marketing.
This book describes the new generation of discrete choice methods,
focusing on the many advances that are made possible by simulation.
Researchers use these statistical methods to examine the choices
that consumers, households, firms, and other agents make. Each of
the major models is covered: logit, generalized extreme value, or
GEV (including nested and cross-nested logits), probit, and mixed
logit, plus a variety of specifications that build on these basics.
Simulation-assisted estimation procedures are investigated and
compared, including maximum stimulated likelihood, method of
simulated moments, and method of simulated scores. Procedures for
drawing from densities are described, including variance reduction
techniques such as anithetics and Halton draws. Recent advances in
Bayesian procedures are explored, including the use of the
Metropolis-Hastings algorithm and its variant Gibbs sampling. The
second edition adds chapters on endogeneity and
expectation-maximization (EM) algorithms. No other book
incorporates all these fields, which have arisen in the past 25
years. The procedures are applicable in many fields, including
energy, transportation, environmental studies, health, labor, and
marketing.
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