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DSGE models in the frequency domain

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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 t, 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 signicantly 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 Classication: C11, C32, E32 Keywords: DSGE models, frequency domain, band maximum likelihood.

Suggested Citation

  • Luca Sala, 2013. "DSGE models in the frequency domain," Working Papers 504, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:504
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    1. repec:eee:macchp:v2-527 is not listed on IDEAS
    2. 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.
    3. 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".
    4. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    5. 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.
    6. repec:eee:jimfin:v:79:y:2017:i:c:p:99-114 is not listed on IDEAS
    7. Wolters, Maik Hendrik, 2018. "How the baby boomers' retirement wave distorts model-based output gap estimates," IMFS Working Paper Series 121, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).

    More about this item

    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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