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Choosing The Variables To Estimate Singular Dsge Models

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  • Fabio Canova
  • Filippo Ferroni
  • Christian Matthes

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 selects 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. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Fabio Canova & Filippo Ferroni & Christian Matthes, 2014. "Choosing The Variables To Estimate Singular Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1099-1117, November.
  • Handle: RePEc:wly:japmet:v:29:y:2014:i:7:p:1099-1117
    DOI: 10.1002/jae.2414
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    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.
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    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.
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    Citations

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    Cited by:

    1. Albonico, Alice & Calès, Ludovic & Cardani, Roberta & Croitorov, Olga & Ferroni, Filippo & Giovannini, Massimo & Hohberger, Stefan & Pataracchia, Beatrice & Pericoli, Filippo & Raciborski, Rafal & Rat, 2017. "The Global Multi-Country Model (GM): an Estimated DSGE Model for the Euro Area Countries," Working Papers 2017-10, Joint Research Centre, European Commission (Ispra site).
    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. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    5. Canova, Fabio & Matthes, Christian, 2018. "A composite likelihood approach for dynamic structural models," CEPR Discussion Papers 13245, C.E.P.R. Discussion Papers.
    6. Iskrev, Nikolay, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Paper Series 2161, European Central Bank.
    7. Maik H. Wolters, 2018. "How the baby boomers' retirement wave distorts model‐based output gap estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 680-689, August.
    8. Albonico, Alice & Calés, Ludovic & Cardani, Roberta & Croitorov, Olga & Ferroni, Filippo & Giovannini, Massimo & Hohberger, Stefan & Pataracchia, Beatrice & Pericoli, Filippo Maria & Raciborski, Rafal, 2019. "Comparing post-crisis dynamics across Euro Area countries with the Global Multi-country model," Economic Modelling, Elsevier, vol. 81(C), pages 242-273.
    9. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    10. 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.
    11. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    12. 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.
    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.
    15. Yongquan Cao & Grey Gordon, 2019. "A Practical Approach to Testing Calibration Strategies," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1165-1182, March.
    16. Tyler Atkinson & Alexander W. Richter & Nathaniel Throckmorton, 2018. "The Zero Lower Bound and Estimation Accuracy," Working Papers 1804, Federal Reserve Bank of Dallas, revised 01 Feb 2019.
    17. Zhongjun Qu, 2018. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 916-932, December.
    18. Fabio Canova & Christian Matthes, 2018. "A composite likelihood approach for dynamic structural models," Working Papers No 10/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    19. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    20. Fabio Canova & Christian Matthes, 2018. "A Composite Likelihood Approach for Dynamic Structural Models," Working Paper 18-12, Federal Reserve Bank of Richmond, revised 23 Jul 2018.
    21. Sergey Ivashchenko & Willi Mutschler, 2019. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," CQE Working Papers 8319, Center for Quantitative Economics (CQE), University of Muenster.

    More about this item

    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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