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On the Robustness of the Balance Statistics with respect to Nonresponse

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Listed author(s):
  • 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 patternscan 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.

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Paper provided by ifo Institute - Leibniz Institute for Economic Research at the University of Munich in its series ifo Working Paper Series with number Ifo Working Paper No. 126.

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Date of creation: 2012
Handle: RePEc:ces:ifowps:_126
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Keywords: Business survey; Monte Carlo study; nonresponse;

Find related papers by 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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  1. 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.
  2. 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.
  3. 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.
  4. Stefan Arent & Alexander Eck & Michael Kloss & Oskar Krohmer, 2012. "Income Risk, Saving and Taxation:Will Precautionary Saving Survive?," ifo Working Paper Series Ifo Working Paper No. 125, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  5. 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.
  6. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
  7. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
  8. Christian Seiler, 2010. "Dynamic Modelling of Nonresponse in Business Surveys," ifo Working Paper Series Ifo Working Paper Nr. 93, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  9. 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.
  10. 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.
  11. 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.
  12. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
  13. 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.
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Cited by:

  1. Robert Lehmann & Antje Weyh, 2014. "Forecasting employment in Europe: Are survey results helpful?," ifo Working Paper Series Ifo Working Paper No. 182, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  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. 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.
  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, 09.
  5. 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.
  6. 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.
  7. 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.

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