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What cycles? Data detrending in DSGE models

Author

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  • Sun Xiaojin

    (University of Texas at El Paso, Economics and Finance, El Paso, TX, USA)

  • Tsang Kwok Ping

    (Virginia Polytechnic Institute and State University, Economics, Blacksburg, VA 24061-0131, USA)

Abstract

It is widely-known that different methods of detrending data yield different business cycle features. The choice of the detrending method, however, is usually arbitrarily made. This paper aims at revealing potential pitfalls of different detrending methods for the estimation of a standard medium-scale DSGE model. By comparing nine popular detrending methods, we find that model parameter estimates, variance decompositions, optimal monetary policies, and out-of-sample forecasting performances of the model are all sensitive to how the data are detrended. We also discuss some possible criteria to choose among different methods.

Suggested Citation

  • Sun Xiaojin & Tsang Kwok Ping, 2019. "What cycles? Data detrending in DSGE models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-23, June.
    • Handle: RePEc:bpj:sndecm:v:23:y:2019:i:3:p:23:n:3
    DOI: 10.1515/snde-2017-0084
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    More about this item

    Keywords

    DSGE models; Data detrending; Monetary policy; Forecasting;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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