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Microdata imputations and macrodata implications: evidence from the Ifo Business Survey

  • Seiler, Christian
  • Heumann, Christian

A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data. But surveys are commonly affected by nonresponding units which can produce biases if these missing values can not be regarded as missing at random. As many papers examined the effect of nonresponse in individual or household surveys, only less is known in the case of business surveys. So, literature leaves a gap on this issue. For this reason, we analyse and impute the missing observations in the Ifo Business Survey, a large business survey in Germany. The most prominent result of this survey is the Ifo Business Climate Index, a leading indicator for the German business cycle. To reflect the underlying latent data generating process, we compare different imputation approaches for longitudinal data. After this, the microdata are aggregated and the results are compared with the original indicators to evaluate their implications on the macro level. Finally, we show that the bias is minimal and ignorable.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 37045.

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Date of creation: 2012
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Handle: RePEc:pra:mprapa:37045
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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.
  2. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
  3. James Honaker & Gary King & Matthew Blackwell, . "Amelia II: A Program for Missing Data," Journal of Statistical Software, American Statistical Association, vol. 45(i07).
  4. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
  5. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer, vol. 95(1), pages 1-26, March.
  6. Christian Seiler, 2010. "Dynamic Modelling of Nonresponse in Business Surveys," Ifo Working Paper Series Ifo Working Paper Nr. 93, Ifo Institute for Economic Research at the University of Munich.
  7. 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.
  8. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
  9. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer, vol. 95(4), pages 351-373, December.
  10. Christian Seiler, 2012. "On the Robustness of the Balance Statistics with respect to Nonresponse," Ifo Working Paper Series Ifo Working Paper No. 126, Ifo Institute for Economic Research at the University of Munich.
  11. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 101-114, February.
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