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Identifying Monetary Policy Shocks via Changes in Volatility

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  • Markku Lanne, Helmut Luetkepohl

Abstract

A central issue of monetary policy analysis is the specification of monetary policy shocks. In a structural vector autoregressive setting there has been some controversy about which restrictions to use for identifying the shocks because standard theories do not provide enough information to fully identify monetary policy shocks. In fact, to compare different theories it would even be desirable to have over-identifying restrictions which would make statistical tests of different theories possible. It is pointed out that some progress towards over-identifying monetary policy shocks can be made by using specific data properties. In particular, it is shown that changes in the volatility of the shocks can be used for identification. Based on monthly US data from 1965-1996 different theories are tested and it is found that associating monetary policy shocks with shocks to nonborrowed reserves leads to a particularly strong rejection of the model whereas assuming that the Fed accommodates demand shocks total reserves cannot be rejected.

Suggested Citation

  • Markku Lanne, Helmut Luetkepohl, 2006. "Identifying Monetary Policy Shocks via Changes in Volatility," Economics Working Papers ECO2006/23, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2006/23
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    References listed on IDEAS

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    1. Markku Lanne & Helmut Lütkepohl, 2008. "Identifying Monetary Policy Shocks via Changes in Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1131-1149, September.
    2. Seth Carpenter & Selva Demiralp, 2008. "The Liquidity Effect in the Federal Funds Market: Evidence at the Monthly Frequency," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(1), pages 1-24, February.
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    More about this item

    Keywords

    Monetary policy; structural vector autoregressive analysis; vector autoregressive process; impulse responses;
    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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