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A fine time for monetary policy?

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Author Info

  • John F. Geweke
  • David E. Runkle

Abstract

Recent research in evaluating the effects of monetary policy is potentially tainted by the problem of time aggregation: that is, effects may be incorrectly estimated using quarterly data if the effects of policy occur rapidly. This study evaluates whether time aggregation is a serious problem in a simple vector autoregression. It shows time aggregation has little impact on evaluating the effect of monetary policy in a simple vector autoregression including total reserves, nonborrowed reserves, and the federal funds rate. This finding suggests that time aggregation is unlikely to be important in evaluating the effects of monetary policy in models including a goal variable, such as GDP growth.

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Bibliographic Info

Article provided by Federal Reserve Bank of Minneapolis in its journal Quarterly Review.

Volume (Year): (1995)
Issue (Month): Win ()
Pages: 18-31

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Handle: RePEc:fip:fedmqr:y:1995:i:win:p:18-31:n:v.19no.1

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Related research

Keywords: Monetary policy;

References

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  1. Christina D. Romer & David H. Romer, 1989. "Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 121-184 National Bureau of Economic Research, Inc.
  2. Steven Strongin, 1992. "The identification of monetary policy disturbances: explaining the liquidity puzzle," Working Paper Series, Macroeconomic Issues 92-27, Federal Reserve Bank of Chicago.
  3. Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-42, October.
  4. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-53, July.
  5. Eric M. Leeper & Christopher A. Sims, 1994. "Toward a modern macroeconomic model usable for policy analysis," Working Paper 94-5, Federal Reserve Bank of Atlanta.
  6. David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report 107, Federal Reserve Bank of Minneapolis.
  7. David B. Gordon & Eric M. Leeper, 1992. "The dynamic impacts of monetary policy: an exercise in tentative identification," Working Paper 92-13, Federal Reserve Bank of Atlanta.
  8. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  9. Romer, Christina D. & Romer, David H., 1994. "Monetary policy matters," Journal of Monetary Economics, Elsevier, vol. 34(1), pages 75-88, August.
  10. Runkle, David E, 1987. "Vector Autoregressions and Reality: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 454, October.
  11. John Coleman & Christian Gilles & Pamela Labadie, 1993. "Identifying monetary policy with a model of the federal funds rate," Finance and Economics Discussion Series 93-24, Board of Governors of the Federal Reserve System (U.S.).
  12. Hoover, Kevin D. & Perez, Stephen J., 1994. "Money may matter, but how could you know?," Journal of Monetary Economics, Elsevier, vol. 34(1), pages 89-99, August.
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Citations

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Cited by:
  1. Berument, Hakan & Togay, Selahattin & Sahin, Afsin, 2011. "Identifying the Liquidity Effects of Monetary Policy Shocks For a Small Open Economy: Turkey," MPRA Paper 46883, University Library of Munich, Germany.
  2. Hakan Berument, 2005. "Measuring Monetary Policy for A Small Open Economy : Turkey," Departmental Working Papers 0509, Bilkent University, Department of Economics.
  3. Redward, Peter & Saarenheimo, Tuomas, 1996. "From Policy Rate to Market Rates: An Empirical Analysis of Finnish Monetary Transmission," Research Discussion Papers 22/1996, Bank of Finland.
  4. Choi, Jae-Young & Ratti, Ronald A., 2000. "The Predictive Power of Alternative Indicators of Monetary Policy," Journal of Macroeconomics, Elsevier, vol. 22(4), pages 581-610, October.

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