Using simulation studies to evaluate statistical methods in Stata: A tutorial

Tim P. Morris (), Ian White and Michael Crowther
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Tim P. Morris: MRC Clinical Trials Unit, University College London, UK
Michael Crowther: University of Leicester

United Kingdom Stata Users' Group Meetings 2016 from Stata Users Group

Abstract: Simulation studies are an invaluable tool for statistical research, particularly for the evaluation of a new method or comparison of competing methods. Simulations are well used by methodologists but often conducted or reported poorly, and are underused by applied statisticians. It's easy to execute a simulation study in Stata, but it's at least as easy to do it wrong. We will describe a systematic approach to getting it right, visiting the following: Types of simulation study; An approach to planning yours; Setting seeds and storing states; Saving estimates with simulate and postfile; Preparing for failed runs and trapping errors; The three types of dataset involved in simulations; Analysis of simulation studies; Presentation of results (including Monte Carlo error). This tutorial will visit concepts, code, tips, tricks, and potholes, with the aim of giving the uninitiated the necessary understanding to start tackling simulation studies.

Date: 2016-09-16
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Citations: View citations in EconPapers (1)

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http://repec.org/usug2016/morris_uksug16.pdf presentation slides (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:boc:usug16:17

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