Simulating quantile models with applications to economics and management

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The massive increase in the speed of computers over the past forty years changed the way that social scientists, applied economists and statisticians approach their trades and also the very nature of the problems that they could feasibly tackle. The new methods that use intensively computer power go by the names of "computer-intensive" or "simulation". My lecture will start with bird's eye view of the uses of simulation in Economics and Statistics. Then I will turn out to my own research on uses of computer- intensive methods. From a methodological point of view the question I address is how to infer marginal distributions having estimated a conditional quantile process, (Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression," Journal of Applied Econometrics 20, 2005). Illustrations will be provided of the use of the method to perform counterfactual analysis in several different areas of knowledge.

Original languageEnglish
Title of host publicationISCM II and EPMESC XII - Proc. of the 2nd Int. Symposium on Computational Mechanics and the 12th Int. Conf. on the Enhancement and Promotion of Computational Methods in Engineering and Science
Pages35-40
Number of pages6
Volume1233
EditionPART 1
DOIs
Publication statusPublished - 2010
Event2nd International Symposium on Computational Mechanics, ISCM II, and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, EPMESC XII - Hong Kong, Macau, China
Duration: 30 Nov 20093 Dec 2009

Conference

Conference2nd International Symposium on Computational Mechanics, ISCM II, and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, EPMESC XII
Country/TerritoryChina
CityHong Kong, Macau
Period30/11/093/12/09

Keywords

  • Bootstrap
  • Quantile regression
  • Resampling

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