The economics and statistics literature using computer simulation based methods has grown enormously over the past decades. Maximum Simulated Likelihood is a statistical tool useful for incorporating individual differences (called heterogeneity in the econometrics literature) and variations into a statistical analysis. Problems that can be intractable with traditional methods are solved using computer simulation integrated with classical methods. Instead of assuming that everyone responds to stimuli in the same way, allowances are made for the possibility that different decision makers will respond in different ways. The techniques can be applied to problems of individual choice, such as the choice of a transportation model, or choice among health care options, as well as to the problem of making financial and macroeconomic predictions. Contributors to the volume discuss alternative simulation methods that permit faster and more accurate inference, as well as applications of established methods.First, a data set from a stock market rarely used in the literature is processed. ... We analyze Tehran stock exchange (TSE) market data set. ... The TSE is open for trading 5 days a week from Saturday to Wednesday, excluding public holidays.
|Title||:||Maximum Simulated Likelihood Methods and Applications|
|Author||:||William H. Greene, R. Carter Hill|
|Publisher||:||Emerald Group Publishing - 2010-12-06|