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On the Robustness of the Balance Statistics with respect to Nonresponse

  • Christian Seiler

Business cycle indicators based on the balance statistics are a widely used method tomonitor the actual economic situation. In contrast to official data, indicators frombusiness 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 aframework 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
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Handle: RePEc:ces:ifowps:_126
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  1. 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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  3. Konstantin A. Kholodilin & Boriss Siliverstovs, 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), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 226(3), pages 234-259, May.
  4. Christian Gayer, 2005. "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.
  5. 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.
  6. Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  7. 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.
  8. 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.
  9. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  10. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
  11. repec:zbw:iwhdps:10-10 is not listed on IDEAS
  12. 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.
  13. Steffen Henzel & Timo Wollmershäuser, 2005. "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.
  14. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
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