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Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal

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Listed:
  • Jan Pablo Burgard
  • Patricia Dörr
  • Ralf Münnich

Abstract

Innovations in statistical methodology is often accompanied by Monte-Carlo studies. In the context of survey statistics two types of inferences have to be considered. First, the classical randomization methods used for developments in statistical modelling. Second, survey data is typically gathered using random sampling schemes from a finite population. In this case, the sampling inference under a finite population model drives statistical conclusions. For empirical analyses, in general, mainly survey data is available. So the question arises how best to conduct the simulation study accompanying the empirical research. In addition, economists and social scientists often use statistical models on the survey data where the statistical inference is based on the classical randomization approach based on the model assumptions. This confounds classical randomization with sampling inference. The question arises under which circumstances – if any – the sampling design can then be ignored. In both fields of research – official statistics and (micro-)econometrics – Monte-Carlo studies generally seek to deliver additional information on an estimator’s distribution. The two named inferences obviously impact distributional assumptions and, hence, must be distinguished in the Monte-Carlo set-up. Both, the conclusions to be drawn and comparability between research results, therefore, depend on inferential assumptions and the consequently adapted simulation study. The present paper gives an overview of the different types of inferences and combinations thereof that are possibly applicable on survey data. Additionally, further types of Monte-Carlo methods are elaborated to provide answers in mixed types of randomization in the survey context as well as under statistical modelling using survey data. The aim is to provide a common understanding of Monte-Carlo based studies using survey data including a thorough discussion of advantages and disadvantages of the different types and their appropriate evaluation.

Suggested Citation

  • Jan Pablo Burgard & Patricia Dörr & Ralf Münnich, 2020. "Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal," Research Papers in Economics 2020-04, University of Trier, Department of Economics.
    • Handle: RePEc:trr:wpaper:202004
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    References listed on IDEAS

    as
    1. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    2. Julian Wagner & Ralf Münnich & Joachim Hill & Johannes Stoffels & Thomas Udelhoven, 2017. "Non‐parametric small area models using shape‐constrained penalized B‐splines," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1089-1109, October.
    3. Jan Pablo Burgard & Patricia Dörr, 2018. "Survey-weighted Generalized Linear Mixed Models," Research Papers in Economics 2018-01, University of Trier, Department of Economics.
    4. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.
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    Keywords

    Monte-Carlo simulation; survey sampling; randomization inference; model inference;
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