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SVARs Identification through Bounds on the Forecast Error Variance

Author

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  • Alessio Volpicella

    (Queen Mary University of London)

Abstract
Sign-restricted Structural Vector Autoregressions (SVARs) are increasingly common. However, they usually result in a set of structural parameters that have very different implications in terms of impulse responses, elasticities, historical decomposition and forecast error variance decomposition (FEVD). This makes it difficult to derive meaningful economic conclusions, and there is always the risk of retaining structural parameters with implausible implications. This paper imposes bounds on the FEVD as a way of sharpening set-identification induced by sign restrictions. Firstly, in a bivariate and trivariate setting, this paper analytically proves that bounds on the FEVD reduce the identified set. For higher dimensional SVARs, I establish the conditions in which the placing of bounds on the FEVD delivers a non-empty set and sharpens inference; algorithms to detect non-emptiness and reduction are also provided. Secondly, under a convexity criterion, a prior-robust approach is proposed to construct estimation and inference. Thirdly, this paper suggests a procedure to derive theory-driven bounds that are consistent with the implications of a variety of popular, but different, DSGE models, with real, nominal, and financial frictions, and with sufficiently wide ranges for their parameters. The methodology is generalized to incorporate uncertainty about the bounds themselves. Fourthly, a Monte-Carlo exercise verifies the effectiveness of those bounds in identifying the data-generating process relative to sign restrictions. Finally, a monetary policy application shows that bounds on the FEVD tend to remove unreasonable implications, increase estimation precision, sharpen and also alter the inference of models identified through sign restrictions.

Suggested Citation

  • Alessio Volpicella, 2019. "SVARs Identification through Bounds on the Forecast Error Variance," Working Papers 890, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:890
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    3. Bouteska, Ahmed & Sharif, Taimur & Hajek, Petr & Abedin, Mohammad Zoynul, 2024. "Aversion and ambiguity: On the robustness of the macroeconomic uncertainty measure framework," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    4. Matthew Read, 2022. "The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions," RBA Research Discussion Papers rdp2022-04, Reserve Bank of Australia.

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    More about this item

    Keywords

    Bounds; Forecast Error Variance; Monetary Policy; Set Identification; Sign Restrictions; Structural Vector Autoregressions (SVARs);
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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