 Monte Carlo Simulation for EconometriciansFoundations and Trends(R) in Econometrics, 2012, vol. 5, issue 1–2, 1-181 Abstract: Many studies in econometric theory are supplemented by Monte Carlo simulation investigations. These illustrate the properties of alternative inference techniques when applied to samples drawn from mostly entirely synthetic data generating processes. They should provide information on how techniques, which may be sound asymptotically, perform in finite samples and then unveil the effects of model characteristics too complex to analyze analytically. Also the interpretation of applied studies should often benefit when supplemented by a dedicated simulation study, based on a design inspired by the postulated actual empirical data generating process, which would come close to bootstrapping. This review presents and illustrates the fundamentals of conceiving and executing such simulation studies, especially synthetic but also more dedicated, focussing on controlling their accuracy, increasing their efficiency, recognizing their limitations, presenting their results in a coherent and palatable way, and on the appropriate interpretation of their actual findings, especially when the simulation study is used to rank the qualities of alternative inference techniques. Keywords: Monte Carlo simulation; Statistical inference methods; Parametric bootstrap techniques; Asymptotic test procedures; Econometrics; Computational Economics; Probability and Statistics (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:now:fnteco:0800000011 Access Statistics for this article More articles in Foundations and Trends(R) in Econometrics from now publishers |
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