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DSGE model meets data gently: The importance of trend modelling

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  • Juvonen, Petteri
  • Sariola, Mikko

Abstract

DSGE models are often specified so that the long-run variation of variables is driven by one or two common trends, which rarely holds in the data. We find that when this discrepancy exists, high-frequency components (measurement errors) capture variable-specific time variation in trends. When high-frequency components are restricted to be small or ignored, the discrepancy is captured by the model component, which distorts shock decompositions. We show that incorporating variable-specific trend components directly into the measurement equations yields a decomposition in which the high-frequency, model, and trend components each capture what they are intended to. We also find trend modelling useful in forecasting.

Suggested Citation

  • Juvonen, Petteri & Sariola, Mikko, 2025. "DSGE model meets data gently: The importance of trend modelling," Bank of Finland Research Discussion Papers 9/2025, Bank of Finland.
    • Handle: RePEc:zbw:bofrdp:325481
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    References listed on IDEAS

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    1. Pierre Lafourcade & Joris de Wind, 2012. "Taking Trends Seriously in DSGE Models: An Application to the Dutch Economy," DNB Working Papers 345, Netherlands Central Bank, Research Department.
    2. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.
    3. Martin Fukač & Adrian Pagan, 2010. "Limited information estimation and evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 55-70, January.
    4. Cogley, Timothy, 2001. "Estimating and testing rational expectations models when the trend specification is uncertain," Journal of Economic Dynamics and Control, Elsevier, vol. 25(10), pages 1485-1525, October.
    5. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    6. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
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    Keywords

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    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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