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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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    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, 2020. "A narrative approach to a fiscal DSGE model," Quantitative Economics, Econometric Society, vol. 11(2), pages 801-837, May.
    4. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    5. Fabio Canova & Christian Matthes, 2021. "A Composite Likelihood Approach for Dynamic Structural Models," The Economic Journal, Royal Economic Society, vol. 131(638), pages 2447-2477.
    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. Adrian Pagan & Tim Robinson, 2020. "Too Many Shocks Spoil the Interpretation," Melbourne Institute Working Paper Series wp2020n02, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    10. 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.
    11. 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.
    12. 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.
    13. William Gatt, 2022. "MEDSEA-FIN: an estimated DSGE model with housing and financial frictions for Malta," CBM Working Papers WP/05/2022, Central Bank of Malta.
    14. Alok Johri & Muhebullah Karimzada, 2021. "Learning efficiency shocks, knowledge capital and the business cycle: A Bayesian evaluation," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(3), pages 1314-1360, November.
    15. Zhongjun Qu & Fan Zhuo, 2021. "Likelihood Ratio-Based Tests for Markov Regime Switching," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(2), pages 937-968.
    16. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.
    17. Nikolay, Iskrev, 2014. "Choosing the variables to estimate singular DSGE models: Comment," Dynare Working Papers 41, CEPREMAP.
    18. 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.
    19. 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.
    20. Atkinson, Tyler & Richter, Alexander W. & Throckmorton, Nathaniel A., 2020. "The zero lower bound and estimation accuracy," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 249-264.
    21. Van Nguyen, Phuong, 2020. "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers 59, CEPREMAP.
    22. Alstadheim, Ragna & Bjørnland, Hilde C. & Maih, Junior, 2021. "Do central banks respond to exchange rate movements? A Markov-switching structural investigation of commodity exporters and importers," Energy Economics, Elsevier, vol. 96(C).
    23. 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.
    24. Morris, Stephen D., 2017. "DSGE pileups," Journal of Economic Dynamics and Control, Elsevier, vol. 74(C), pages 56-86.
    25. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.

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    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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