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REAL-TIME MONITORING OF THE US INFLATION EXPECTATION PROCESS

Published online by Cambridge University Press:  10 January 2018

Vasyl Golosnoy  and
Jan Roestel
Vasyl Golosnoy*
Affiliation:
Ruhr–Universität–Bochum
Jan Roestel
Affiliation:
Christian–Albrechts–Universität zu Kiel
*
Address correspondence to: Vasyl Golosnoy, Faculty of Management and Economics, Ruhr–Universität–Bochum, Universitätstraße 150, D-44801 Bochum, Germany; e-mail: [email protected].
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Abstract

Real-time supervision of shifts in inflation expectations is an important issue for monetary policy makers, especially in the presence of economic uncertainty. In this paper, we elaborate tools for on-line monitoring of such shifts by extracting valuable information from noisy daily financial market data. For this purpose, first, we suggest a new risk adjustment for observable proxies of medium and long run inflation expectations assuming that the latter are well-anchored. Second, we propose an econometric methodology for sequential monitoring of level changes in the associated proxies at daily frequency. Our empirical evidence shows that the on-line surveillance of risk adjusted US forward breakeven inflation rates by means of the cumulative sum (CUSUM) detector appears to be helpful to extract timely signals on potential shifts. In particular, the obtained signals indicate important turning points in market-based measures of inflation expectations, which also tend to materialize in lower frequency experts' surveys.

Keywords

Breakeven InflationCUSUMEarly WarningsExpected InflationMonetary PolicyOn-Line Monitoring
Type
Articles
Information
Macroeconomic Dynamics , Volume 23 , Issue 6 , September 2019 , pp. 2221 - 2249
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This research has been in part financially supported by the Collaborative Research Center “Statistical modeling of nonlinear dynamic processes” (SFB 823, Teilprojekt A1) of the German Research Foundation (DFG). We are also indebted to two anonymous referees as well as to the associate editor for their helpful and constructive remarks and suggestions.

References

REFERENCES

Andersson, E., Bock, D., and Frisén, M. (2005) Statistical surveillance of cyclical processes with application to turns in business cycles. Journal of Forecasting 24, 465–490.Google Scholar
Andersen, T. G., Bollerslev, T., and Diebold, F. (2007) Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics & Statistics 89, 701–720.Google Scholar
Ball, L. (1992) Why does high inflation raise inflation uncertainty? Journal of Monetary Economics 29, 371–88.Google Scholar
Barinov, A. (2014) Turnover: liquidity or uncertainty? Management Science 60, 2478–2495.Google Scholar
Baxa, J., Horvath, R., and Vasicek, B. (2014) How does monetary policy change? Evidence on inflation-targeting countries. Macroeconomic Dynamics 18, 593–630.Google Scholar
Beechey, M. J. and Wright, J. H. (2008) The high-frequency impact of news on long-term yields and forward rates: Is it real? Journal of Monetary Economics 56, 535–544.Google Scholar
Bernanke, B. S. (2004) What policymakers can learn from asset prices? Speech Before The Investment Analysts Society of Chicago. Chicago, Illinois, http://www.federalreserve.gov/boarddocs/speeches/2004/20040415/Google Scholar
Branch, W. A. (2004) The theory of rationally heterogeneous expectations: Evidence from survey data on inflation expectations. The Economic Journal 114, 592–621.Google Scholar
Breitung, J. and Homm, U. (2012) Testing for speculative bubbles in stock markets: A comparison of alternative methods. Journal of Financial Econometrics 10, 198–231.Google Scholar
Cavaliere, G. and Taylor, A. M. R. (2008) Bootstrap unit root tests for time series with nonstationary volatility. Econometric Theory 24, 43–71.Google Scholar
Cecchetti, S. G. and Moessner, R. (2008) Commodity prices and inflation dynamics. BIS Quarterly Review December 2008, 55–66.Google Scholar
Chernow, M. and Müller, P. (2012) The term structure of inflation expectations. Journal of Financial Economics 106, 367–394.Google Scholar
Christensen, Jens H. E., Lopez, J. A., and Rudebusch, G. D. (2010) Inflation expectations and risk premiums in an arbitrage-free model of nominal and real bond yields. Journal of Money, Credit and Banking 42, 143–178.Google Scholar
Christensen, Jens H. E. and Gillan, James M. (2012) Could the U.S. Treasury Benefit from Issuing More TIPS? Working paper 2011-16, Federal Reserve Bank of San Francisco.Google Scholar
Chu, C. S., Stinchcombe, M., and White, H. (1996) Monitoring structural change. Econometrica 64, 1045–1065.Google Scholar
Friedman, M. (1977) Nobel lecture: Inflation and unemployment. Journal of Political Economy 85, 451–72.Google Scholar
Frisén, M. (ed.) (2008) Financial Surveillance. Chichester, England: Wiley.Google Scholar
Francq, C. and Zakoïan, J.-M. (2004) Maximum likelihood estimation of pure GARCH and ARMA–GARCH processes. Bernoulli 10, 605–637.Google Scholar
Galati, G., Poelhekke, S. and Zhou, Chen (2011) Did the crisis affect inflation expectations? International Journal of Central Banking 7: 167–207.Google Scholar
Garthoff, R., Golosnoy, V. and Schmid, W. (2013) Monitoring the mean of multivariate financial time series. Applied Stochastic Models in Business and Industry 30, 328–340.Google Scholar
Golosnoy, V. and Hogrefe, J. (2013) Signaling NBER turning points: A sequential approach. Journal of Applied Statistics 40, 438–448.Google Scholar
Golosnoy, V., Ragulin, S. and Schmid, W. (2009) Multivariate CUSUM chart: Properties and enhancements. AStA Advances in Statistical Analysis 93, 263–279.Google Scholar
Golosnoy, V., Okhrin, I. and Schmid, W. (2012) Statistical surveillance of volatility forecasting models. Journal of Financial Econometrics 10, 513–543.Google Scholar
Gorr, W. L. and Ord, J. K. (2009) Introduction to time series monitoring. International Journal of Forecasting 25, 463–466.Google Scholar
Grothe, M. and Meyler, A. (2015) Inflation Forecasts: Are Market-Based and Survey-Based Measures Informative? MPRA working paper no. 66982.Google Scholar
Gürkaynak, R. S., Sack, B., and Wright, J. H. (2010a) The TIPS yield curve and inflation compensation. American Economic Journal: Macroeconomics 2, 70–92.Google Scholar
Gürkaynak, R. S., Levin, A., and Swanson, E. (2010b) Does inflation targeting anchor long-run inflation expectations? Evidence from the US, UK and Sweden. Journal of the European Economic Association 8, 1208–1242.Google Scholar
Hawkins, D. M. (1992) Evaluation of average run lengths of cumulative sum charts for an arbitrary data distribution. Communications in Statistics – Simulation and Computation 21, 1001–1020.Google Scholar
Henzel, S. and Wieland, E. (2017) International synchronization and changes in long-term inflation uncertainty. Macroeconomic Dynamics 21, 918–946.Google Scholar
Herwartz, H. and Roestel, J. (2009) Monetary independence under floating exchange rates: Evidence based on international breakeven inflation rates. Studies in Nonlinear Dynamics & Econometrics 13 (4), 1–25.Google Scholar
Hördahl, P. and Tristani, O. (2010) Inflation Risk Premia in the US and the Euro Area. BIS working paper no. 325.Google Scholar
Ireland, P. N. (2007) Changes in the Federal Reserve's inflation target: Causes and consequences. Journal of Money, Credit and Banking 39, 1851–1882.Google Scholar
Johnson, T. C. (2008) Volume, liquidity and liquidity risk. Journal of Financial Economics 87, 388–417.Google Scholar
MacEachern, S. N., Rao, Y., and Wu, C. (2007) A robust-likelihood cumulative sum chart. Journal of American Statistical Association 102, 1440–1447.Google Scholar
Montgomery, D. C. (2013) Statistical Quality Control, 7th ed. New York: Wiley.Google Scholar
Moustakides, G. V. (1986) Optimal stopping times for detecting changes in distributions. Annals of Statistics 14, 1379–1387.Google Scholar
Moustakides, G. V. (2008) Sequential change detection revisited. Annals of Statistics 36, 787–807.Google Scholar
Ord, J. K., Koehler, A. B., Snyder, R. D. and Hyndman, R. J. (2009) Monitoring processes with changing variances. International Journal of Forecasting 25, 518–525.Google Scholar
Page, E. S. (1954) Continuous inspection schemes. Biometrika 41, 100–114.Google Scholar
Pastor, L. and Stambaugh, R. F. (2003) Liquidity risk and expected stock returns. Journal of Political Economy 111, 642–685.Google Scholar
Rafiq, S. (2014) Decomposing UK inflation expectations using survey-based measures. Macroeconomic Dynamics 18, 1508–1538.Google Scholar
Rogerson, P. A. (2006) Formulas for the design of CUSUM quality control charts. Communications in Statistics – Theory and Methods 35, 373–383.Google Scholar
Sack, B. and Elsasser, R. (2004) Treasury inflation-indexed debt: A review of the US experience. Economic Policy Review 10 (1), 47–63.Google Scholar
Salmon, C. (2015) Financial market volatility and liquidity – A cautious note. Speech of the Executive Director, Markets, Bank of England, www.bankofengland.co.uk/publications/Pages/speeches/2015/809.pdfGoogle Scholar
Schmid, W. and Tzotchev, D. (2004) Statistical surveillance of the parameters of a one-factor Cox-Ingersoll-Ross model. Sequential Analysis 23, 379–412.Google Scholar
Siegmund, D. (1985) Sequential Analysis. New York: Springer.Google Scholar
Söderlind, (2011) Inflation risk premia and survey evidence on macroeconomic uncertainty. International Journal of Central Banking 7, 113–133.Google Scholar
Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P. and Woodall, W. H. (2000) The state of statistical process control as we proceed into the 21st century. Journal of the American Statistical Association 95, 992–998.Google Scholar
Strohsal, T. and Winkelmann, L. (2015) Assessing the anchoring of inflation expectations. Journal of International Money and Finance 50, 33–48.Google Scholar
Trehan, B. (2015) Survey measures of expected inflation and the inflation process. Journal of Money, Credit and Banking 47, 207–222.Google Scholar
Woodall, W. H. (2000) Controversies and contradictions in statistical process control. Journal of Quality Technology 32, 341–378.Google Scholar
Zeileis, A., Leisch, F., Kleiber, C. and Hornik, K. (2005) Monitoring structural change in dynamic econometric models. Journal of Applied Econometrics 20, 99–121.Google Scholar