Advanced Search
MyIDEAS: Login to save this paper or follow this series

A Bayesian evaluation of alternative models of trend inflation

Contents:

Author Info

  • Todd E. Clark
  • Taeyoung Doh

Abstract

With the concept of trend inflation now widely understood as to be important as a measure of the public's perception of the inflation goal of the central bank and important to the accuracy of longer-term inflation forecasts, this paper uses Bayesian methods to assess alternative models of trend inflation. Reflecting models common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation, including: AR with constant trend; AR with trend equal to last period's inflation rate; local level model; AR with random walk trend; AR with trend equal to the long-run expectation from the Survey of Professional Forecasters; and AR with time-varying parameters. We consider versions of the models with constant shock variances and with stochastic volatility. We first use Bayesian metrics to compare the fits of the alternative models. We then use Bayesian methods of model averaging to account for uncertainty surrounding the model of trend inflation, to obtain an alternative estimate of trend inflation in the U.S. and to generate medium-term, model-average forecasts of inflation. Our analysis yields two broad results. First, in model fit and density forecast accuracy, models with stochastic volatility consistently dominate those with constant volatility. Second, for the specification of trend inflation, it is difficult to say that one model of trend inflation is the best. Among alternative models of the trend in core PCE inflation, the local level specification of Stock and Watson (2007) and the survey-based trend specification are about equally good. Among competing models of trend GDP inflation, several trend specifications seem to be about equally good.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.clevelandfed.org/research/workpaper/2011/wp1134.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1134.

as in new window
Length:
Date of creation: 2011
Date of revision:
Handle: RePEc:fip:fedcwp:1134

Contact details of provider:
Postal: 1455 East 6th St., Cleveland OH 44114
Phone: 216.579.2000
Web page: http://www.clevelandfed.org/
More information through EDIRC

Order Information:
Email:

Related research

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

Other versions of this item:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Todd E. Clark & Michael W. McCracken, 2007. "Averaging forecasts from VARs with uncertain instabilities," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2007-42, Board of Governors of the Federal Reserve System (U.S.).
  3. 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).
  4. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
  5. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2008. "Inflation-Gap Persistence in the U.S," NBER Working Papers 13749, National Bureau of Economic Research, Inc.
  6. Koop, Gary & Korobilis, Dimitris, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," MPRA Paper 20125, University Library of Munich, Germany.
  7. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers, University of Brescia, Department of Economics ubs0504, University of Brescia, Department of Economics.
  8. Todd E. Clark & Troy Davig, 2009. "Decomposing the declining volatility of long-term inflation expectations," Research Working Paper, Federal Reserve Bank of Kansas City RWP 09-05, Federal Reserve Bank of Kansas City.
  9. 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," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2011-33, Scottish Institute for Research in Economics (SIRE).
  10. Todd E. Clark & Michael W. McCracken, 2009. "Nested forecast model comparisons: a new approach to testing equal accuracy," Research Working Paper, Federal Reserve Bank of Kansas City RWP 09-11, Federal Reserve Bank of Kansas City.
  11. John Geweke, 1998. "Using simulation methods for Bayesian econometric models: inference, development, and communication," Staff Report, Federal Reserve Bank of Minneapolis 249, Federal Reserve Bank of Minneapolis.
  12. Todd Clark & Michael W. McCracken, 2011. "Advances in forecast evaluation," Working Paper 1120, Federal Reserve Bank of Cleveland.
  13. Elmar Mertens, 2011. "Measuring the level and uncertainty of trend inflation," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2011-42, Board of Governors of the Federal Reserve System (U.S.).
  14. Luc Bauwens & Gary Koop & Dimitris Korobilis & Jeroen V.K. Rombouts, 2011. "A Comparison of Forecasting Procedures for Macroeconomic Series: the Contribution of Structural Break Models," Cahiers de recherche, CIRPEE 1104, CIRPEE.
  15. Peter N. Ireland, 2005. "Changes in the Federal Reserve’s Inflation Target: Causes and Consequences," Boston College Working Papers in Economics, Boston College Department of Economics 607, Boston College Department of Economics.
  16. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
  17. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper, Norges Bank 2008/22, Norges Bank.
  18. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series, European Central Bank 0969, European Central Bank.
  19. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
  20. Todd E. Clark & Michael W. McCracken, 2010. "Testing for unconditional predictive ability," Working Papers, Federal Reserve Bank of St. Louis 2010-031, Federal Reserve Bank of St. Louis.
  21. 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, Elsevier, vol. 7(2), pages 151-174.
  22. Bauwens, Luc & Korobilis, Dimitris & Koop, Gary, 2011. "A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2011-25, Scottish Institute for Research in Economics (SIRE).
  23. Michael Dotsey & Shigeru Fujita & Tom Stark, 2011. "Do Phillips curves conditionally help to forecast inflation?," Working Papers 11-40, Federal Reserve Bank of Philadelphia.
  24. Kadiyala, K. Rao & Karlsson, Sune, 1994. "Numerical Aspects of Bayesian VAR-modeling," Working Paper Series in Economics and Finance 12, Stockholm School of Economics.
  25. Watson, Mark W., 1986. "Univariate detrending methods with stochastic trends," Journal of Monetary Economics, Elsevier, Elsevier, vol. 18(1), pages 49-75, July.
  26. Michael T. Kiley, 2008. "Monetary policy actions and long-run inflation expectations," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2008-03, Board of Governors of the Federal Reserve System (U.S.).
  27. Robert J. Gordon, 1998. "Foundations of the Goldilocks Economy: Supply Shocks and the Time-Varying NAIRU," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(2), pages 297-346.
  28. James Morley & Jeremy Piger, 2012. "The Asymmetric Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 208-221, February.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. 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.
  2. Chan, Joshua & Koop, Gary & Potter, Simon, 2012. "A New Model Of Trend Inflation," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2012-12, Scottish Institute for Research in Economics (SIRE).
  3. Joshua C C Chan & Gary Koop & Simon M Potter, 2012. "A New Model of Trend Inflation," CAMA Working Papers 2012-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. Gefang, Deborah & Koop, Gary & Potter, Simon M., 2008. "The Dynamics of UK and US Inflation Expectations," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2008-59, Scottish Institute for Research in Economics (SIRE).
  5. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Working Papers, Queen Mary, University of London, School of Economics and Finance 720, Queen Mary, University of London, School of Economics and Finance.
  6. Gefang, Deborah & Koop, Gary & Potter, Simon M., 2011. "The Dynamics of UK and US Inflation Expectations," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2011-47, Scottish Institute for Research in Economics (SIRE).
  7. Deborah Gefang & Gary Koop & Simon M. Potter, 2009. "The Dynamics of UK and US Inflation Expectations," Working Paper Series, The Rimini Centre for Economic Analysis 14_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  8. Taeyoung Doh, 2011. "Is unemployment helpful in understanding inflation?," Economic Review, Federal Reserve Bank of Kansas City, Federal Reserve Bank of Kansas City, issue Q IV, pages 5-26.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:fip:fedcwp:1134. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lee Faulhaber).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.