DSGE model meets data gently: The importance of trend modelling

Petteri Juvonen and Mikko Sariola

No 9/2025, Bank of Finland Research Discussion Papers from Bank of Finland

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.

Keywords: DSGE; trends; business cycles (search for similar items in EconPapers)
JEL-codes: C52 C53 E17 E32 E37 (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-dge and nep-mac
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