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Object-oriented bayesian networks for modelling the respondent measurement error

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  • Daniela Marella
  • Paola Vicard

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Abstract

In this paper Object-Oriented Bayesian networks are proposed as a tool to model measurement errors in a categorical variable due to respondent. A mixed measurement error model is presented and an Object-Oriented Bayesian network implementing such a model is introduced. The insertion of evidence represented by the observed value and its propagation throughout the network yields for each unit the probability distribution of the true value given the observed. Two methods are used to predict the individual true value and their performance is evaluated via simulation.

Suggested Citation

  • Daniela Marella & Paola Vicard, 2012. "Object-oriented bayesian networks for modelling the respondent measurement error," Departmental Working Papers of Economics - University 'Roma Tre' 0167, Department of Economics - University Roma Tre.
  • Handle: RePEc:rtr:wpaper:0167
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    File URL: http://dipeco.uniroma3.it/public/WP%20167.pdf
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    1. Michael Woodford, 2005. "Firm-Specific Capital and the New Keynesian Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    2. Juillard, Michael & Kamenik, Ondra & Kumhof, Michael & Laxton, Douglas, 2008. "Optimal price setting and inflation inertia in a rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2584-2621, August.
    3. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    4. Miles S. Kimball & John G. Fernald & Susanto Basu, 2006. "Are Technology Improvements Contractionary?," American Economic Review, American Economic Association, vol. 96(5), pages 1418-1448, December.
    5. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2004. "The Response of Hours to a Technology Shock: Evidence Based on Direct Measures of Technology," Journal of the European Economic Association, MIT Press, vol. 2(2-3), pages 381-395, 04/05.
    6. Harvey, A. C. & Stock, James H., 1988. "Continuous time autoregressive models with common stochastic trends," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 365-384.
    7. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
    8. Frank Schorfheide, 2008. "DSGE model-based estimation of the New Keynesian Phillips curve," Economic Quarterly, Federal Reserve Bank of Richmond, issue Fall, pages 397-433.
    9. Vlaar, Peter J. G., 2004. "Shocking the eurozone," European Economic Review, Elsevier, vol. 48(1), pages 109-131, February.
    10. Marco Del Negro & Frank Schorfheide & Frank Smets & Raf Wouters, 2004. "On the fit and forecasting performance of New Keynesian models," FRB Atlanta Working Paper 2004-37, Federal Reserve Bank of Atlanta.
    11. Anthony Garratt & Kevin Lee & M. Hashem Pesaran & Yongcheol Shin, 2003. "A Long run structural macroeconometric model of the UK," Economic Journal, Royal Economic Society, vol. 113(487), pages 412-455, April.
    12. Søren Johansen & Rocco Mosconi & Bent Nielsen, 2000. "Cointegration analysis in the presence of structural breaks in the deterministic trend," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 216-249.
    13. Jordi Galí & Mark Gertler, 2007. "Macroeconomic Modeling for Monetary Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 25-46, Fall.
    14. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    15. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    16. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    17. Mark Bils & Peter J. Klenow, 2004. "Some Evidence on the Importance of Sticky Prices," Journal of Political Economy, University of Chicago Press, vol. 112(5), pages 947-985, October.
    18. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, pages 586-606.
    19. Kimball, Miles S, 1995. "The Quantitative Analytics of the Basic Neomonetarist Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(4), pages 1241-1277, November.
    20. Jordi Gali Garreta & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations; How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
    21. Orphanides, Athanasios, 2003. "Monetary policy evaluation with noisy information," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 605-631, April.
    22. Fabio Canova & David Lopez-Salido & Claudio Michelacci, 2010. "The effects of technology shocks on hours and output: a robustness analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 755-773.
    23. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, pages 431-449.
    24. Matthew Shapiro & Mark Watson, 1988. "Sources of Business Cycles Fluctuations," NBER Chapters,in: NBER Macroeconomics Annual 1988, Volume 3, pages 111-156 National Bureau of Economic Research, Inc.
    25. Johansen, Soren, 1995. "Identifying restrictions of linear equations with applications to simultaneous equations and cointegration," Journal of Econometrics, Elsevier, vol. 69(1), pages 111-132, September.
    26. Beaudry, Paul & Portier, Franck, 2007. "When can changes in expectations cause business cycle fluctuations in neo-classical settings?," Journal of Economic Theory, Elsevier, vol. 135(1), pages 458-477, July.
    27. MacKinnon, James G & Haug, Alfred A & Michelis, Leo, 1999. "Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 563-577, Sept.-Oct.
    28. Pesaran, M. Hashem & Smith, Ron, 1995. "The role of theory in econometrics," Journal of Econometrics, Elsevier, vol. 67(1), pages 61-79, May.
    29. Francesco Giuli & Massimiliano Tancioni, 2010. "Contractionary Effects of Supply Shocks: Evidence and Theoretical Interpretation," Working Papers 131, University of Rome La Sapienza, Department of Public Economics.
    30. Sveen, Tommy & Weinke, Lutz, 2007. "Firm-specific capital, nominal rigidities, and the Taylor principle," Journal of Economic Theory, Elsevier, vol. 136(1), pages 729-737, September.
    31. Renee Fry & Adrian Pagan, 2005. "Some Issues In Using Vars For Macroeconometric Research," CAMA Working Papers 2005-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Francis, Neville & Ramey, Valerie A., 2005. "Is the technology-driven real business cycle hypothesis dead? Shocks and aggregate fluctuations revisited," Journal of Monetary Economics, Elsevier, pages 1379-1399.
    33. Jordi Gali Garreta & Pau Rabanal, 2004. "Technology Shocks and Aggregate Fluctuations; How Well Does the RBC Model Fit Postwar U.S. Data?," IMF Working Papers 04/234, International Monetary Fund.
    34. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
    35. Athanasios Orphanides, 2007. "Taylor rules," Finance and Economics Discussion Series 2007-18, Board of Governors of the Federal Reserve System (U.S.).
    36. Giuli, Francesco & Tancioni, Massimiliano, 2012. "Real rigidities, productivity improvements and investment dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 100-118.
    37. Sveen, Tommy & Weinke, Lutz, 2005. "New perspectives on capital, sticky prices, and the Taylor principle," Journal of Economic Theory, Elsevier, vol. 123(1), pages 21-39, July.
    38. Dedola, Luca & Neri, Stefano, 2007. "What does a technology shock do? A VAR analysis with model-based sign restrictions," Journal of Monetary Economics, Elsevier, vol. 54(2), pages 512-549, March.
    39. Susanto Basu, 1998. "Technology and business cycles; how well do standard models explain the facts?," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 42(Jun), pages 207-269.
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    More about this item

    Keywords

    Bayesian networks; Measurement errors; Respondent Error.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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