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The low-frequency impact of daily monetary policy shocks

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  • Neville Francis
  • Eric Ghysels
  • Michael T. Owyang

Abstract

With rare exception, studies of monetary policy tend to neglect the timing of the innovations to the monetary policy instrument. Models which do take timing seriously are often difficult to compare to standard VAR models of monetary policy because of the differences in the frequency that they use. We propose an alternative model using MIDAS regressions which nests both ideas: Accurate (daily) timing of innovations to the monetary policy instrument are embedded in a monthly frequency VAR to determine the macroeconomic effects of high frequency changes to policy. We find that taking into account the timing of the shocks is important and can alleviate some of the puzzles in standard monthly VARs [e.g., the price puzzle]. We find that policy shocks are most important to variables thought of as being heavily expectations-oriented and that, contrary to some VAR studies, the effects of FOMC shocks on real variables are small.>

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2011-009.

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Date of creation: 2011
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Handle: RePEc:fip:fedlwp:2011-009

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Keywords: Monetary policy ; Econometric models ; Prices;

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References

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  1. Refet S. Gürkaynak & Brian Sack & Eric Swanson, 2002. "Market-based measures of monetary policy expectations," Finance and Economics Discussion Series 2002-40, Board of Governors of the Federal Reserve System (U.S.).
  2. Emanuel Moench & Serena Ng & Simon Potter, 2013. "Dynamic Hierarchical Factor Model," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1811-1817, December.
  3. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 0633, European Central Bank.
  4. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
  5. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
  6. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
  8. Neville Francis & Michael T. Owyang, 2004. "Monetary policy in a Markov-switching VECM: implications for the cost of disinflation and the price puzzle," Working Papers 2003-001, Federal Reserve Bank of St. Louis.
  9. Peter A. Zadrozny, 1990. "Forecasting U.S. GNP at monthly intervals with an estimated bivariate time series model," Economic Review, Federal Reserve Bank of Atlanta, issue Nov, pages 2-15.
  10. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank, Research Centre.
  11. Hanson, Michael S., 2004. "The "price puzzle" reconsidered," Journal of Monetary Economics, Elsevier, vol. 51(7), pages 1385-1413, October.
  12. Stefan Mittnik & Peter A. Zadrozny, 2004. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly IFO Business Conditions Data," CESifo Working Paper Series 1203, CESifo Group Munich.
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Cited by:
  1. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.

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