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Choosing the variables to estimate singular DSGE models

Author

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  • Canova, Fabio
  • Ferroni, Filippo
  • Matthes, Christian

Abstract

We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second the vector that minimizes the informational discrepancy between the singular and non-singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for applied researchers are provided.

Suggested Citation

  • Canova, Fabio & Ferroni, Filippo & Matthes, Christian, 2013. "Choosing the variables to estimate singular DSGE models," CEPR Discussion Papers 9381, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:9381
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    References listed on IDEAS

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    1. Luca Sala & Ulf Soderstrom & Antonella Trigari, 2010. "The Output Gap, the Labor Wedge, and the Dynamic Behavior of Hours," Working Papers 365, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    3. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    4. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    5. John H. Cochrane, 2011. "Determinacy and Identification with Taylor Rules," Journal of Political Economy, University of Chicago Press, vol. 119(3), pages 565-615.
    6. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    7. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    8. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2009. "New Keynesian Models: Not Yet Useful for Policy Analysis," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 242-266, January.
    9. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    10. Pablo A. Guerron-Quintana, 2010. "What you match does matter: the effects of data on DSGE estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 774-804.
    11. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    12. Zhongjun Qu & Denis Tkachenko, 2010. "Identification and Frequency Domain QML Estimation of Linearized DSGE Models," Boston University - Department of Economics - Working Papers Series WP2010-053, Boston University - Department of Economics.
    13. Kleibergen, Frank & Mavroeidis, Sophocles, 2009. "Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 293-311.
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    Cited by:

    1. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Bank of England working papers 527, Bank of England.
    2. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," LSE Research Online Documents on Economics 86320, London School of Economics and Political Science, LSE Library.
    3. Thorsten Drautzburg, 2014. "A Narrative Approach to a Fiscal DSGE Model," 2014 Meeting Papers 791, Society for Economic Dynamics.
    4. Yongquan Cao & Grey Gordon, 2016. "A Practical Approach to Testing Calibration Strategies," Caepr Working Papers 2016-004 Classification-C, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    5. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    6. repec:eee:macchp:v2-527 is not listed on IDEAS
    7. Wolters, Maik Hendrik, 2016. "How the Baby Boomers' Retirement Wave Distorts Model-Based Output Gap Estimates," Annual Conference 2016 (Augsburg): Demographic Change 145812, Verein für Socialpolitik / German Economic Association.
    8. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    9. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Zhongjun Qu & Fan Zhuo, 2015. "Likelihood Ratio Based Tests for Markov Regime Switching," Boston University - Department of Economics - Working Papers Series wp2015-003, Boston University - Department of Economics.
    11. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    12. Zhongjun Qu, 2015. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," Boston University - Department of Economics - Working Papers Series wp2015-002, Boston University - Department of Economics.
    13. Nikolay, Iskrev, 2014. "Choosing the variables to estimate singular DSGE models: Comment," Dynare Working Papers 41, CEPREMAP.
    14. Massimo Franchi, 2013. "Comment on: Ravenna, F., 2007. Vector autoregressions and reduced form representations of DSGE models. Journal of Monetary Economics 54, 2048-2064," DSS Empirical Economics and Econometrics Working Papers Series 2013/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.

    More about this item

    Keywords

    ABCD representation; Density ratio; DSGE models.; Identification;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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