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A Bayesian evaluation of alternative models of trend inflation

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  • 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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  3. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
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Citations

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Cited by:
  1. Deborah Gefang & Gary Koop & Simon Potter, 2011. "The Dynamics of UK and US Inflation Expectations," Working Papers 1120, University of Strathclyde Business School, Department of Economics.
  2. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Birkbeck Working Papers in Economics and Finance 1409, Birkbeck, Department of Economics, Mathematics & Statistics.
  3. Taeyoung Doh, 2011. "Is unemployment helpful in understanding inflation?," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-26.
  4. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2013. "A New Model of Trend Inflation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 94-106, January.
  5. 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.

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