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Qualitative versus Quantitative External Information for Proxy Vector Autoregressive Analysis

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  • Lukas Boer
  • Helmut Lütkepohl

Abstract

A major challenge for proxy vector autoregressive analysis is the construction of a suitable external instrument variable or proxy for identifying a shock of interest. Some authors construct sophisticated proxies that account for the dating and size of the shock while other authors consider simpler versions that use only the dating and signs of particular shocks. It is shown that such qualitative (sign-)proxies can lead to impulse response estimates of the impact effects of the shock of interest that are nearly as efficient as or even more efficient than estimators based on more sophisticated quantitative proxies that also reflect the size of the shock. Moreover, the sign-proxies tend to provide more precise impulse response estimates than an approach based merely on the higher volatility of the shocks of interest on event dates.

Suggested Citation

  • Lukas Boer & Helmut Lütkepohl, 2021. "Qualitative versus Quantitative External Information for Proxy Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 1940, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1940
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    2. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    3. Martin Bruns & Helmut Lütkepohl & James McNeil, 2024. "Avoiding Unintentionally Correlated Shocks in Procy Vector Autoregressive Analysis," Discussion Papers of DIW Berlin 2095, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    GMM; heteroskedastic VAR; instrumental variable estimation; proxy VAR; structural vector autoregression;
    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

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