Too many shocks spoil the interpretation

Adrian Pagan and Tim Robinson

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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 Unobserved-Component (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)
Pages: 41 pages
Date: 2020-03
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-ets and nep-mac
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Too Many Shocks Spoil the Interpretation (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2020-28

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