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Monte Carlo Simulation for Econometricians

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  • Kiviet, Jan F.

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.

Suggested Citation

  • Kiviet, Jan F., 2012. "Monte Carlo Simulation for Econometricians," Foundations and Trends(R) in Econometrics, now publishers, vol. 5(1–2), pages 1-181, March.
  • Handle: RePEc:now:fnteco:0800000011
    DOI: 10.1561/0800000011
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    Citations

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    Cited by:

    1. Evžen Kočenda & Karen Poghosyan, 2018. "Export Sophistication: A Dynamic Panel Data Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(12), pages 2799-2814, September.
    2. Gillespie, Jeffrey & Qushim, Berdikul & Nyaupane, Narayan & McMillin, Kenneth, 2015. "Breeding Technologies in U.S. Meat Goat Production: Who Are the Adopters and How Does Adoption Impact Productivity?," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(3), pages 1-25, December.
    3. Jan F. Kiviet & Qu Feng, 2014. "Efficiency Gains by Modifying GMM Estimation in Linear Models under Heteroskedasticity," UvA-Econometrics Working Papers 14-06, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Kiviet, Jan F. & Pleus, Milan, 2017. "The performance of tests on endogeneity of subsets of explanatory variables scanned by simulation," Econometrics and Statistics, Elsevier, vol. 2(C), pages 1-21.
    5. Kiviet, Jan F. & Phillips, Garry D.A., 2014. "Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 424-448.
    6. Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.
    7. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    8. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    9. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.
    10. repec:ags:afjare:225658 is not listed on IDEAS
    11. Annalivia Polselli, 2023. "Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples," Papers 2312.17676, arXiv.org.
    12. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.

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