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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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    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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