IDEAS home Printed from https://ideas.repec.org/b/ces/ifobei/52.html
   My bibliography  Save this book

Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence

Author

Listed:
  • Christian Seiler

Abstract

Zur Beantwortung sozioökonomischer Fragestellungen nehmen Umfragen als Methode für den empirischen Erkenntnisgewinn eine zentrale Rolle ein. Ein weitverbreitetes Problem auf der Erhebungsseite ist jedoch das Auftreten fehlender Werte, welche zu verzerrten Ergebnissen führen können. Während es für Bevölkerungs- und Haushaltsbefragungen eine umfassende Literatur zu diesem Thema existiert, ist dieses Thema im Bereich von Unternehmensbefragungen in weit geringerem Maße erforscht worden. Diese Arbeit widmet sich den fehlenden Werten im ifo Konjunkturtest, welcher in ähnlicher Form in fast allen OECD-Ländern durchgeführt wird. Das prominenteste Ergebnis dieser monatlich durchgeführten Umfrage ist der ifo Geschäftsklimaindikator, ein Konjunkturindikator für die deutsche Wirtschaft, welcher große Beachtung bei Unternehmern, Analysten, Politikern, Journalisten, Wissenschaftlern und in der breiten Öffentlichkeit findet. Die Ergebnisse dieser Arbeit zeigen, dass Konjunkturindikatoren basierend auf dieser Form der Befragung sehr stabil bezüglich nicht-zufälligen Ausfallprozessen sind. Dies lässt sich sowohl mit Hilfe von Simulationsstudien als auch durch die Schätzung der fehlenden Werte zeigen. Insbesondere führen die fehlenden Werte nicht zu einer Verschlechterung der Prognoseleistung des ifo Geschäftsklimaindikators.

Suggested Citation

  • 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, November.
  • Handle: RePEc:ces:ifobei:52
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/ifo_Beitraege_z_Wifo_52.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. 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.
    4. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
    5. 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.
    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. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    8. 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.
    9. Patrick Royston, 2005. "Multiple imputation of missing values: Update of ice," Stata Journal, StataCorp LP, vol. 5(4), pages 527-536, December.
    10. Horst Rottmann & Timo Wollmershäuser, 2013. "A micro data approach to the identification of credit crunches," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2423-2441, June.
    11. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    12. Jörg Drechsler, 2011. "Multiple imputation in practice—a case study using a complex German establishment survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(1), pages 1-26, March.
    13. Samson B. Adebayo & Ludwig Fahrmeir & Christian Seiler & Christian Heumann, 2011. "Geoadditive Latent Variable Modeling of Count Data on Multiple Sexual Partnering in Nigeria," Biometrics, The International Biometric Society, vol. 67(2), pages 620-628, June.
    14. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053.
    15. 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.
    16. Schnabel Annette, 1997. "Teilnahmeverhalten bei Unternehmensbefragungen," Arbeit, De Gruyter, vol. 6(2), pages 154-172, June.
    17. 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.
    18. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    19. Adrian Mander, 2003. "WHOTDECK: Stata module to perform multiple imputation using the Approximate Bayesian Bootstrap with weights," Statistical Software Components S433201, Boston College Department of Economics, revised 21 Feb 2011.
    20. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    21. 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.
    22. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    23. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo, vol. 56(2), pages 192-220, June.
    24. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    25. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    26. M. Hashem Pesaran & Allan Timmermann, 2006. "Testing Dependence among Serially Correlated Multi-category Variables," CESifo Working Paper Series 1770, CESifo.
    27. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    28. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    29. 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.
    30. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    31. 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.
    32. Klaus Abberger & Manuel Birnbrich & Christian Seiler, 2009. "Der »Test des Tests« im Handel – eine Metaumfrage zum ifo Konjunkturtest," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 34-41, November.
    33. repec:zbw:iwhdps:10-10 is not listed on IDEAS
    34. 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.
    35. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    36. Su, Yu-Sung & Gelman, Andrew & Hill, Jennifer & Yajima, Masanao, 2011. "Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i02).
    37. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
    38. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael Weinhardt & Alexia Meyermann & Stefan Liebig & Jürgen Schupp, 2016. "The Linked Employer-Employee Study of the Socio-Economic Panel (SOEP-LEE): Project Report," SOEPpapers on Multidisciplinary Panel Data Research 829, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. Matthias Bannert & Andreas Dibiasi, 2014. "Unveiling Participant Level Determinants of Unit Non-Response in Business Tendency Surveys," KOF Working papers 14-363, KOF Swiss Economic Institute, ETH Zurich.
    3. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    2. 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.
    3. Christian Seiler, 2012. "The Data Sets of the LMU-ifo Economics & Business Data Center – A Guide for Researchers," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 132(4), pages 609-618.
    4. Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
    5. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    6. 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.
    7. Yasutomo Murasawa, 2013. "Measuring Inflation Expectations Using Interval-Coded Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(4), pages 602-623, August.
    8. Anja Hönig, 2010. "Linkage of Ifo Survey and Balance-Sheet Data: The EBDC Business Expectations Panel & the EBDC Business Investment Panel," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 130(4), pages 635-642.
    9. 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, November.
    10. Sebastian Link, 2018. "Harmonization and Interpretation of the ifo Business Survey's Micro Data," CESifo Working Paper Series 7427, CESifo.
    11. Kajal Lahiri & Yongchen Zhao, 2013. "Quantifying Heterogeneous Survey Expectations: The Carlson-Parkin Method Revisited," Discussion Papers 13-08, University at Albany, SUNY, Department of Economics.
    12. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    13. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    14. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2017. "Forecasting GDP all over the World: Evidence from Comprehensive Survey Data," MPRA Paper 81772, University Library of Munich, Germany.
    15. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 55.
    16. Jaroslav Borovicka, 2016. "Identifying ambiguity shocks in business cycle models using survey data," 2016 Meeting Papers 1615, Society for Economic Dynamics.
    17. Tomislav Globan & Vladimir Arčabić & Petar Sorić, 2016. "Inflation in New EU Member States: A Domestically or Externally Driven Phenomenon?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(1), pages 154-168, January.
    18. Massenot, Baptiste & Pettinicchi, Yuri, 2018. "Can firms see into the future? Survey evidence from Germany," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 66-79.
    19. Zeno Enders & Franziska Hünnekes & Gernot Müller, 2019. "Firm expectations and economic activity," CESifo Working Paper Series 7623, CESifo.
    20. Sarah M. Lein & Thomas Maag, 2011. "The Formation Of Inflation Perceptions: Some Empirical Facts For European Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(2), pages 155-188, May.

    More about this item

    Keywords

    Business Survey; Ifo Business Climate; Imputation; Missing data; Panel survey; Unit nonresponse;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • 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
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ifobei:52. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Klaus Wohlrabe). General contact details of provider: https://edirc.repec.org/data/ifooode.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.