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A Bayesian evaluation of alternative models of trend inflation

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Author Info

  • Todd E.Clark
  • Taeyoung Doh

Abstract

The concept of trend inflation is important in making accurate inflation forecasts. However, there is little consensus on how the trend in inflation should be modeled. While some studies suggest a survey-based measure of long-run inflation expectations as a good empirical proxy for trend inflation, others have argued for a statistical exercise of decomposing inflation data into trend and cycle components. In this paper, we assess alternative models of trend inflation based on the accuracy of medium-term inflation forecasts. To incorporate recent evidence on the time-varying macroeconomic volatility, we consider models with both constant volatility and time-varying volatility. For all the models, we compare not only point predictions but also density forecasts, such as deflation probability. Our analysis yields two broad results. First, models with time-varying volatility consistently dominate those with constant volatility. Second, once time-varying volatility is incorporated, it is difficult to say that one model of trend inflation is better. Simply averaging forecasts with time-varying volatility is as good as forecasts from the best-fitting model. In addition, the relative performance of each model varies greatly over time. Overall, our results suggest that it is important to consider predictions from a range of models with time-varying volatility.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Kansas City in its series Research Working Paper with number RWP 11-16.

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Date of creation: 2011
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Handle: RePEc:fip:fedkrw:rwp11-16

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Keywords: Bayesian statistical decision theory ; Inflation (Finance) ; Forecasting;

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References

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  1. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
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  3. BAUWENS, Luc & KOOP, Gary & KOROBILIS, Dimitris & ROMBOUTS, Jeroen V. K., 2011. "A comparison of forecasting procedures for macroeconomic series: the contribution of structural break models," CORE Discussion Papers 2011003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Todd Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Paper 1120, Federal Reserve Bank of Cleveland.
  5. Elmar Mertens, 2011. "Measuring the level and uncertainty of trend inflation," Finance and Economics Discussion Series 2011-42, Board of Governors of the Federal Reserve System (U.S.).
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  9. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
  10. Cogley, Timothy W. & Morozov, Sergei & Sargent, Thomas J., 2003. "Bayesian fan charts for UK inflation: Forecasting and sources of uncertainty in an evolving monetary system," CFS Working Paper Series 2003/44, Center for Financial Studies (CFS).
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  16. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers 2010-031, Federal Reserve Bank of St. Louis.
  17. Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Working Papers 2009-050, Federal Reserve Bank of St. Louis.
  18. James Morley & Jeremy Piger, 2012. "The Asymmetric Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 208-221, February.
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Citations

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Cited by:
  1. Deborah Gefang & Gary Koop & Simon M. Potter, 2009. "The Dynamics of UK and US Inflation Expectations," Working Paper Series 14_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  2. Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A new model of trend inflation," MPRA Paper 39496, University Library of Munich, Germany.
  3. Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. Taeyoung Doh, 2011. "Is unemployment helpful in understanding inflation?," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-26.

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