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On the Robustness of the Balance Statistics with respect to Nonresponse

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  • Christian Seiler

Abstract

Business cycle indicators based on the balance statistics are a widely used method to monitor the actual economic situation. In contrast to official data, indicators from business surveys are early available and typically not revised after their first publication. But as surveys can be in general affected by distortions through the response behaviour, these indicators can also be biased. In addition, time-dependent nonresponse patterns can produce even more complex forms of biased results. This paper examines a framework which kind of nonresponse patterns lead to biases and decreases in performance. We perform an extensive Monte Carlo study to analyse their effects on the indicators. Our analyses show that these indicators are extremely stable towards selection biases.

Suggested Citation

  • Christian Seiler, 2012. "On the Robustness of the Balance Statistics with respect to Nonresponse," ifo Working Paper Series 126, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  • Handle: RePEc:ces:ifowps:_126
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    File URL: http://www.cesifo-group.de/DocDL/IfoWorkingPaper-126.pdf
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    Cited by:

    1. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5 is not listed on IDEAS
    2. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ERSA conference papers ersa15p756, European Regional Science Association.
    3. David Leuwer & Bernd Süssmuth, 2013. "The Exchange Rate Susceptibility of Some European Core Industries and the Currency Union," CESifo Working Paper Series 4253, CESifo Group Munich.
    4. Christian Seiler, 2012. "Zur Robustheit des ifo Geschäftsklimaindikators in Bezug auf fehlende Werte," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 65(17), pages 19-22, September.
    5. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    6. Leuwer, David & Süßmuth, Bernd, 2017. "The exchange rate susceptibility of European core industries, 1995-2010," Working Papers 147, University of Leipzig, Faculty of Economics and Management Science.
    7. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52, April.
    8. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.

    More about this item

    Keywords

    Business survey; Monte Carlo study; nonresponse;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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