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Dsge Models in the Frequency Domains

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  • Luca Sala

Abstract

We use frequency domain techniques to estimate a medium-scale DSGE model on different frequency bands. We show that goodness of fit, forecasting performance and parameter estimates vary substantially with the frequency bands over which the model is estimated. Estimates obtained using subsets of frequencies are characterized by significantly different parameters, an indication that the model cannot match all frequencies with one set of parameters. In particular, we find that: i) the low frequency properties of the data strongly affect parameter estimates obtained in the time domain; ii) the importance of economic frictions in the model changes when different subsets of frequencies are used in estimation. This is particularly true for the investment cost friction and habit persistence: when low frequencies are present in the estimation, the investment cost friction and habit persistence are estimated to be higher than when low frequencies are absent. JEL Classification: C11, C32, E32 Keywords: DSGE models, frequency domain, band maximum likelihood.
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Suggested Citation

  • Luca Sala, 2015. "Dsge Models in the Frequency Domains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 219-240, March.
    • Handle: RePEc:wly:japmet:v:30:y:2015:i:2:p:219-240
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    Cited by:

    1. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    2. Ho, Paul, 2024. "Estimating the effects of demographics on interest rates: A robust Bayesian perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 158(C).
    3. Fratianni, Michele & Gallegati, Marco & Giri, Federico, 2022. "The medium-run Phillips curve: A time–frequency investigation for the UK," Journal of Macroeconomics, Elsevier, vol. 73(C).
    4. Mario Forni & Luca Gambetti & Luca Sala, 2016. "VAR Information and the Empirical Validation of DSGE Models," Center for Economic Research (RECent) 119, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    5. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    6. Meyer-Gohde, Alexander, 2024. "Solving and analyzing DSGE models in the frequency domain," IMFS Working Paper Series 207, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    7. Lubik, Thomas A. & Matthes, Christian & Verona, Fabio, 2019. "Assessing U.S. aggregate fluctuations across time and frequencies," Bank of Finland Research Discussion Papers 5/2019, Bank of Finland.
    8. Caraiani, Petre & Gupta, Rangan, 2020. "Is the response of the bank of England to exchange rate movements frequency-dependent?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    9. Gallegati, Marco & Giri, Federico & Palestrini, Antonio, 2019. "DSGE model with financial frictions over subsets of business cycle frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 152-163.
    10. Kliem, Martin & Kriwoluzky, Alexander & Sarferaz, Samad, 2016. "Monetary–fiscal policy interaction and fiscal inflation: A tale of three countries," European Economic Review, Elsevier, vol. 88(C), pages 158-184.
    11. 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.
    12. Dieppe, Alistair & Francis, Neville & Kindberg-Hanlon, Gene, 2021. "The identification of dominant macroeconomic drivers: coping with confounding shocks," Working Paper Series 2534, European Central Bank.
    13. 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.
    14. Ross Doppelt & Keith O'Hara, 2018. "Bayesian Estimation of Fractionally Integrated Vector Autoregressions and an Application to Identified Technology Shocks," 2018 Meeting Papers 1212, Society for Economic Dynamics.
    15. Carlos Medel, 2014. "The Typical Spectral Shape of An Economic Variable: A Visual Guide with 100 Examples," Working Papers Central Bank of Chile 719, Central Bank of Chile.
    16. Gehrke, Britta & Yao, Fang, 2017. "Are supply shocks important for real exchange rates? A fresh view from the frequency-domain," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 99-114.
    17. Meyer-Gohde, Alexander & Tzaawa-Krenzler, Mary, 2023. "Sticky information and the Taylor principle," IMFS Working Paper Series 189, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    18. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    19. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    20. Majid M. Al-Sadoon, 2020. "The Spectral Approach to Linear Rational Expectations Models," Papers 2007.13804, arXiv.org, revised Aug 2024.

    More about this item

    Keywords

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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