Seiler, Christian and Heumann, Christian (2012): Microdata imputations and macrodata implications: evidence from the Ifo Business Survey.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Microdata imputations and macrodata implications: evidence from the Ifo Business Survey|
|Keywords:||Business survey, Longitudinal data, Imputation, Nonresponse|
|Subjects:||C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access
C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C83 - Survey Methods ; Sampling Methods
|Depositing User:||Christian Seiler|
|Date Deposited:||02 Mar 2012 20:10|
|Last Modified:||19 Sep 2016 21:43|
Abberger, K. and Wohlrabe, K. (2006). Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur. ifo Schnelldienst, 59(22):19–26.
Anderson, O. (1951). Konjunkturtest und Statistik. Allgemeines Statistisches Archiv, 35:209–220.
Anderson, O. (1952). The business test of the IFO-Institute for Economic Research. Revue del’Institute International de Statistique, 20:1–17.
Becker, S. O. and Wohlrabe, K. (2008). Micro Data at the Ifo Institute for Economic Research - The "Ifo Business Survey", Usage and Access. Journal of Applied Social Science Studies, 128(2):307–319.
Cameron, A. C. and Trivedi, P. K. (2005). Microeconometrics. Methods and Applications. Cambridge University Press.
Chen, J. and Shao, J. (2000). Nearest Neighbor Imputation for Survey Data. Journal of Official Statistics, 16(2):113-131.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20:37–46.
Cook, R. J., Zeng, L., and Yi, G. Y. (2004). Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation. Biometrics, 60(3):820–828.
Destatis (2008). Classification of Economic Activities, Edition 2008. Federal Statistical Office of Germany.
Engels, J. M. and Diehr, P. (2003). Imputation of missing longitudinal data: a comprison of methods. Journal of Clinical Epidemiology, 56:968–976.
European Union (2006). Joint Harmonised EU Programme of Business and Consumer Surveys. Official Journal of the European Union, 49(C 245):5–8.
Finch,W. H. (2010). Imputation Methods for Missing Categorical Questionnaire Data: A Comparison of Approaches. Journal of Data Science, 8:361–378.
Giacomini, R. and White, H. (2006). Tests of Conditional Predictive Abilitys. Econometrica, 74(6):1545–1578.
Goldrian, G., editor (2007). Handbook of survey- based business cycle analysis. Edward Elgar Publishing.
Graham, J. W., Olchowski, A. E., and Gilreath, T. D. (2007). How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory. Preventative Science, 8:208–213.
Harris-Kojetin, B. and Tucker, C. (1999). Exploring the Relation of Economical and Political Conditions with Refusal Rates to a Government Survey. Journal of Official Statistics, 15(2):167–184.
Honaker, J. and King, G. (2010). What to do About Missing Values in Time Series Cross-Section Data. American Journal of Political Science, 54(2):561–581.
Honaker, J., King, G., and Blackwell, M. (2011). Amelia II: A Program for Missing Data.
Janik, F. and Kohaut, S. (2011). Why don’t they answer? - Unit non-response in the IAB Establishment Panel. Quality & Quantity. to appear.
King, G., Honaker, J., Joseph, A., and Scheve, K. (2001). Analyzing incomplete political science data: An alternative algorithm for multiple imputation. American Political Science Review, 95(1):49–69.
Kleinke, K., Stemmler, M., Reinecke, J., and Lösel, F. (2011). Efficient ways to impute incomplete panel data. AStA Advances in Statistical Analysis. to appear.
Landis, J. R. and Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159–174.
Little, R. J. A. and Rubin, D. (2002). Statistical Analysis with Missing Data. Wiley.
Manski, C. (2003). Partial Identification of Probability Distributions. Springer.
McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B, 42:109-142.
Nardo, M. (2003). The quantification of qualitative survey data: A critical assessment. Journal of Economic Surveys, 17(5):645–668.
OECD (2003). Business Tendency Surveys - A Handbook.
Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley.
Saha, C. and Jones, M. P. (2009). Bias in the last observation carried forward method under informative dropout. Journal of Statistical Planning and Inference, 139(2):246–255.
Schafer, J. (1997). Analysis of Incomplete Multivariate Data. Chapman and Hall.
Schunk, D. (2008). A Markov chain Monte Carlo algorithm for multiple imputation in large surveys. AStA Advances in Statistical Analysis, 92(1):101–114.
Seiler, C. (2010). Dynamic Modelling of Nonresponse in Business Surveys. Ifo Working Paper 93, Ifo Institute.
Seiler, C. and Wohlrabe, K. (2012). Surveys, Nonresponse, and the Business Cycle. CESifo Working Paper, Ifo Institute. to appear.
Theil, H. (1952). On the shape of economic microvariables and the Munich business test. Revue del’Institute International de Statistique, 20:105–120.
Woolley, S. B., Cardoni, A. A., and Goethe, J. W. (2009). Last-observationcarried-forward imputation method in clinical efficacy trials: review of 352 antidepressant studies. Pharmacotherapy, 29(12):1408–1416.