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Microdata Adjustment by the Minimum Information Loss Principle

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  • Merz, Joachim

Abstract

Microdata have become increasingly important for economic and social analyses. One striking problem with almost any practical analysis of microdata, microdata as a singular cross or longitudinal sample or within (static) microsimulation, is to achieve representative results. In this study a consistent solution of the microdata adjustment problem - that is to achieve representative results by re-weighting microdata to fit aggregate control data - is presented based on the Minimum Information Loss (MIL) principle. Based on information theory this principle satisfies the desired positivity constraint on the weighting factors to be computed. For the consistent solution which simultaneously adjusts hierarchical microdata (e.g. household and personal information), a fast numerical solution by a specific modified Newton-Raphson (MN) procedure with a global exponential approximation is proposed. Practical experiences for large microdata sets in a pension reform analysis with e.g. more than 60.000 households and 240 restrictions simultaneously to be achieved within the Sfb 3 microsimulation model show that this MN procedure was able to rather largely reduce the computional expenses by 75%. The available efficient PC-computer program ADJUST is also succesfully applied in a described microsimulation analyses of the recent 1990 German tax reform investigating the impacts on market and non-market labour supply within the formal and informal economy, and in a recent firm microsimulation analysion explaining factors of successful firms in the German engineering industry.

Suggested Citation

  • Merz, Joachim, 1994. "Microdata Adjustment by the Minimum Information Loss Principle," MPRA Paper 7231, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:7231
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    References listed on IDEAS

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    1. Merz, Joachim, 1994. "Microsimulation - A Survey of Methods and Applications for Analyzing Economic and Social Policy," MPRA Paper 7232, University Library of Munich, Germany.
    2. Merz, Joachim, 1991. "Microsimulation -- A survey of principles, developments and applications," International Journal of Forecasting, Elsevier, vol. 7(1), pages 77-104, May.
    3. Finke, Renate & Theil, Henri, 1984. "An extended version of minimum information estimation of allocation models," Economics Letters, Elsevier, vol. 15(3-4), pages 229-233.
    4. Merz, Joachim, 1993. "Microsimulation as an Instrument to Evaluate Economic and Social Programmes," MPRA Paper 7236, University Library of Munich, Germany.
    5. Merz, Joachim & Wolff, Klaus G, 1993. "The Shadow Economy: Illicit Work and Household Production: A Microanalysis of West Germany," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 39(2), pages 177-194, June.
    6. Theil, Henri & Finke, Renate & Flood, Lennart R., 1984. "Minimum information estimation of allocation models," Economics Letters, Elsevier, vol. 15(3-4), pages 251-256.
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    Citations

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    Cited by:

    1. Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
    2. Joachim Merz & Dominik Hanglberger & Rafael Rucha, 2009. "The Timing of Daily Demand for Goods and Services – Multivariate Probit Estimates and Microsimulation Results for an Aged Population with German Time Use Diary Data," FFB-Discussionpaper 77, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    3. Merz, Joachim, 1995. "MICSIM : Concept, Developments and Applications of a PC-Microsimulation Model for Research and Teaching," MPRA Paper 16029, University Library of Munich, Germany.
    4. Pierre-Richard AGÉNOR & Derek H. C. CHEN & Michael GRIMM, "undated". "Linking Representative Household Models with Household Surveys for Poverty Analysis: A Comparison of Alternative Methodologies," EcoMod2004 330600002, EcoMod.
    5. Joachim Merz & Henning Stolze, 2005. "Representative Time Use Data and Calibration of the American Time Use Studies 1965-1999," FFB-Discussionpaper 54, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg, revised Jan 2006.
    6. Joachim Merz, 2002. "Zur Kumulation von Haushaltsstichproben," FFB-Discussionpaper 37, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    7. Grösche, Peter & Schröder, Carsten, 2011. "Eliciting public support for greening the electricity mix using random parameter techniques," Energy Economics, Elsevier, vol. 33(2), pages 363-370, March.
    8. Joachim Merz & Paul Böhm & Dominik Hanglberger & J.F. Rafael Rucha & Henning Stolze, 2007. "Wann werden Serviceleistungen nachgefragt? Ein Mikrosimulationsmodell alternativer Ladenöffnungszeiten mit Daten der Zeitbudgeterhebung ServSim," FFB-Discussionpaper 70, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    9. Joachim Merz & Henning Stolze, 2008. "Representative time use data and new harmonised calibration of the American Heritage Time Use Data (AHTUD) 1965-1999," electronic International Journal of Time Use Research, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)) and The International Association for Time Use Research (IATUR), vol. 5(1), pages 90-126, November.
    10. Schatz, Christof & Merz, Joachim, 2000. "Die Rentenreform in der Diskussion Ein Mikrosimulationsmodell für die Altersvorsorge in Deutschland (AVID-PRO)," MPRA Paper 7328, University Library of Munich, Germany.
    11. Widmaier, Ulrich & Niggemann, Hiltrud & Merz, Joachim, 1994. "What makes the Difference between Unsuccessful and Successful Firms in the German Mechanical Engineering Industry?," MPRA Paper 7230, University Library of Munich, Germany.
    12. Joachim Merz & Dominik Hanglberger & Rafael Rucha, 2010. "The Timing of Daily Demand for Goods and Services—Microsimulation Policy Results of an Aging Society, Increasing Labour Market Flexibility, and Extended Public Childcare in Germany," Journal of Consumer Policy, Springer, vol. 33(2), pages 119-141, June.

    More about this item

    Keywords

    Microdata Adjustment; Microanalyses; Microsimulation; Minimum Information Loss; Modified Newton- Raphson Algorithm; PC program package ADJUST;

    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
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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