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The Impact of Uncertainty Shocks under Measurement Error. A Proxy SVAR Approach

Author

Listed:
  • Andrea Carriero

    (Queen Mary, University of London)

  • Haroon Mumtaz

    (Queen Mary University of London)

  • Konstantinos Theodoridis

    (Bank of England)

  • Angeliki Theophilopoulou

    (University of Westminister)

Abstract

A growing empirical literature has considered the impact of uncertainty using SVAR models that include proxies for uncertainty shocks as endogenous variables. In this paper we consider the possible impact of measurement error in the uncertainty shock proxies on the estimated impulse responses from these SVAR models. We show via a Monte Carlo experiment that measurement error can result in attenuation bias in the SVAR impulse responses. In contrast, the proxy SVAR that uses the uncertainty shock proxy as an instrument to identify the underlying shock does not suffer from this bias. Applying this proxy SVAR method to the Bloom (2009) data set results in estimated impulse responses to uncertainty shocks that are larger in magnitude and persistence than those obtained from a standard recursive SVAR.

Suggested Citation

  • Andrea Carriero & Haroon Mumtaz & Konstantinos Theodoridis & Angeliki Theophilopoulou, 2013. "The Impact of Uncertainty Shocks under Measurement Error. A Proxy SVAR Approach," Working Papers 707, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:707
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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