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The Low-Frequency Impact of Daily Monetary Policy Shock

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  • Neville Francis

    (University of North Carolina, Chapel Hill)

Abstract

With rare exception, studies of monetary policy tend to neglect the timing of innovations to monetary policy instruments. Models which take timing seriously are often difficult to compare to standard monetary VARs because each uses different frequencies. We propose using MIDAS regressions that nests both ideas: Accurate (daily) timing of innovations to policy are embedded in a monthly-frequency VAR to determine the macroeconomic effects of high-frequency policy shocks. We find that policy have greatest effects on variables thought of as heavily expectations oriented and that, contrary to some VAR studies, the effects of policy shocks on real variables are small.

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  • Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    • Handle: RePEc:red:sed012:198
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    1. 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.
    2. Kareken, John H & Muench, Thomas & Wallace, Neil, 1973. "Optimal Open Market Strategy: The Use of Information Variables," American Economic Review, American Economic Association, vol. 63(1), pages 156-172, March.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Christiano, Lawrence J & Eichenbaum, Martin & Evans, Charles, 1996. "The Effects of Monetary Policy Shocks: Evidence from the Flow of Funds," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 16-34, February.
    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. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    7. 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.
    8. Faust, Jon & Swanson, Eric T. & Wright, Jonathan H., 2004. "Identifying VARS based on high frequency futures data," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1107-1131, September.
    9. Kuttner, Kenneth N., 2001. "Monetary policy surprises and interest rates: Evidence from the Fed funds futures market," Journal of Monetary Economics, Elsevier, vol. 47(3), pages 523-544, June.
    10. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    11. 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.
    12. Gurkaynak, Refet S. & Sack, Brian T. & Swanson, Eric P., 2007. "Market-Based Measures of Monetary Policy Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 201-212, April.
    13. Froyen, Richard T. & Waud, Roger N., 2002. "The determinants of Federal Reserve policy actions: A re-examination," Journal of Macroeconomics, Elsevier, vol. 24(3), pages 413-428, September.
    14. Stefan Mittnik & Peter Zadrozny, 2005. "Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data," Contributions to Economics, in: Jan-Egbert Sturm & Timo Wollmershäuser (ed.), Ifo Survey Data in Business Cycle and Monetary Policy Analysis, pages 19-48, Springer.
    15. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    16. LeRoy, Stephen F & Waud, Roger N, 1977. "Applications of the Kalman Filter in Short-Run Monetary Control," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(1), pages 195-207, February.
    17. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    18. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    19. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense? A Reply," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 943-948, November.
    20. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    21. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    22. Leon, Angel & Nave, Juan M. & Rubio, Gonzalo, 2007. "The relationship between risk and expected return in Europe," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 495-512, February.
    23. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    24. 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.
    25. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    26. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    27. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    28. Monika Piazzesi, 2002. "The Fed and Interest Rates - A High-Frequency Identification," American Economic Review, American Economic Association, vol. 92(2), pages 90-95, May.
    29. Francis, Neville & Owyang, Michael T., 2005. "Monetary Policy in a Markov-Switching Vector Error-Correction Model: Implications for the Cost of Disinflation and the Price Puzzle," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 305-313, July.
    30. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 907-931, November.
    31. Hanson, Michael S., 2004. "The "price puzzle" reconsidered," Journal of Monetary Economics, Elsevier, vol. 51(7), pages 1385-1413, October.
    32. Cook, Timothy & Hahn, Thomas, 1989. "The effect of changes in the federal funds rate target on market interest rates in the 1970s," Journal of Monetary Economics, Elsevier, vol. 24(3), pages 331-351, November.
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    1. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.

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