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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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    1. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    2. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    3. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
    4. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    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. Christian Gayer, 2006. "Forecast Evaluation of European Commission Survey Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(2), pages 157-183.
    7. Stefan Arent & Alexander Eck & Michael Weber & Oskar Krohmer, 2012. "Income Risk, Saving and Taxation: Will Precautionary Saving Survive?," ifo Working Paper Series 125, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. Christian Seiler, 2010. "Dynamic Modelling of Nonresponse in Business Surveys," ifo Working Paper Series 93, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    10. Breitung, Jörg & Schmeling, Maik, 2013. "Quantifying survey expectations: What’s wrong with the probability approach?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 142-154.
    11. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    12. Steffen Henzel & Timo Wollmershäuser, 2006. "Quantifying Inflation Expectations with the Carlson-Parkin Method: A Survey-based Determination of the Just Noticeable Difference," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 321-352.
    13. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
    14. Anna Stangl, 2007. "European Data Watch: Ifo World Economic Survey Micro Data," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(3), pages 487-496.
    15. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data : A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
    16. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
    17. Florian Janik & Susanne Kohaut, 2012. "Why don’t they answer? Unit non-response in the IAB establishment panel," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 917-934, April.
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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, 2016. "Wirtschaftswachstum und Konjunkturprognosen auf regionaler Ebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65, April.
    3. 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.
    4. 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.
    5. 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.
    6. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ERSA conference papers ersa15p756, European Regional Science Association.
    7. 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.
    8. 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.
    9. 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.

    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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