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Too many shocks spoil the interpretation

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  • Adrian Pagan
  • Tim Robinson

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.

Suggested Citation

  • Adrian Pagan & Tim Robinson, 2020. "Too many shocks spoil the interpretation," CAMA Working Papers 2020-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2020-28
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    References listed on IDEAS

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    Cited by:

    1. Georgiadis, Georgios & Jančoková, Martina, 2020. "Financial globalisation, monetary policy spillovers and macro-modelling: Tales from 1001 shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    2. Ilabaca, Francisco & Milani, Fabio, 2021. "Heterogeneous expectations, indeterminacy, and postwar US business cycles," Journal of Macroeconomics, Elsevier, vol. 68(C).
    3. Lütkepohl, Helmut, 2020. "Structural vector autoregressive models with more shocks than variables identified via heteroskedasticity," Economics Letters, Elsevier, vol. 195(C).
    4. Helmut Lütkepohl, 2020. "Structural Vector Autoregressive Models with More Shocks than Variables Identified via Heteroskedasticity," Discussion Papers of DIW Berlin 1871, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Partial Information; Structural Shocks; Kalman Filter; Measurement Error; DSGE;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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