Too Many Shocks Spoil the Interpretation

Adrian Pagan and Tim Robinson

Melbourne Institute Working Paper Series from Melbourne Institute of Applied Economic and Social Research, The University of Melbourne

Abstract: We show that when a model has more shocks than observed variables the estimated filtered and smoothed shocks will be correlated. This is despite no correlation being present in the data generating process. Additionally the estimated shock innovations may be autocorrelated. These correlations limit the relevance of impulse responses, which assume uncorrelated shocks, for interpreting the data. Excess shocks occur frequently, e.g. in UnobservedComponent (UC) models, filters, including Hodrick-Prescott (1997), and some Dynamic Stochastic General Equilibrium (DSGE) models. Using several UC models and an estimated DSGE model, Ireland (2011), we demonstrate that sizable correlations among the estimated shocks can result.

Keywords: Partial Information; Structural Shocks; Kalman Filter; Measurement Error; DSGE. (search for similar items in EconPapers)
JEL-codes: C51 C52 E37 (search for similar items in EconPapers)
Date: 2020-02
New Economics Papers: this item is included in nep-dge, nep-ets and nep-mac
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Citations: View citations in EconPapers (4)

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Working Paper: Too many shocks spoil the interpretation (2020)
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