Learning Trend Inflation – Can Signal Extraction Explain Survey Forecasts?
It can be shown that inflation expectations and associated forecast errors are characterized by a high degree of persistence. One reason may be that forecasters cannot directly observe the inflation target pursued by the central bank and, hence, face a complicated forecasting problem. In particular, they have to infer whether the observed movement ofthe inflation rate is due to a permanent change of policy parameters or whether it is the result of a transient shock. Consequently, it is assumed that agents behave like econometricians who filter noisy information by estimating an unobserved components model. This constitutes the trend learning algorithm employed by the forecaster. To examine whether this is a valid assumption, I fit a simple learning model to US survey expectations. The second part contains an out-of-sample forecasting experiment which shows that learning by signal extraction matches survey measures closer than other standard models. Moreover, it turns out that a weighted average of different expectation formation processes with a prominent role for signal extraction behaviour is well suited to explain survey measures of inflation expectations.
|Date of creation:||2008|
|Date of revision:|
|Contact details of provider:|| Postal: Poschingerstr. 5, 81679 München|
Web page: http://www.cesifo-group.de
More information through EDIRC
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.:
- David Andolfatto & Scott Hendry & Kevin Moran, 2007.
"Are Inflation Expectations Rational?,"
Working Paper Series
27_07, The Rimini Centre for Economic Analysis.
- Maarten Dossche & Gerdie Everaert, 2005.
"Measuring inflation persistence: A structural time series approach,"
Money Macro and Finance (MMF) Research Group Conference 2005
85, Money Macro and Finance Research Group.
- M. Dossche & G. Everaert, 2005. "Measuring inflation persistence: a structural time series approach," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/340, Ghent University, Faculty of Economics and Business Administration.
- Maarten Dossche & Gerdie Everaert, 2005. "Measuring inflation persistence: a structural time series approach," Working Paper Research 70, National Bank of Belgium.
- Maarten Dossche & Gerdie Everaert, 2005. "Measuring Inflation Persistence: A Structural Time Series Approach," Computing in Economics and Finance 2005 459, Society for Computational Economics.
- Dossche, Maarten & Everaert, Gerdie, 2005. "Measuring inflation persistence: a structural time series approach," Working Paper Series 0495, European Central Bank.
- David Andolfatto & Paul Gomme, 2001.
"Monetary policy regimes and beliefs,"
9905, Federal Reserve Bank of Cleveland.
- David Andolfatto & Paul Gomme, 1997. "Monetary Policy Regimes and Beliefs," Cahiers de recherche CREFE / CREFE Working Papers 48, CREFE, Université du Québec à Montréal, revised Apr 2001.
- George W. Evans & Seppo Honkapohja, 2004.
"Adaptive learning and monetary policy design,"
- George W. Evans & Seppo Honkapohja, 2002. "Adaptive Learning and Monetary Policy Design," University of Oregon Economics Department Working Papers 2002-18, University of Oregon Economics Department, revised 04 Mar 2004.
- Evans, George W. & Honkapohja, Seppo, 2002. "Adaptive learning and monetary policy design," Research Discussion Papers 29/2002, Bank of Finland.
- Evans, George W. & Honkapohja, Seppo, 2003. "Adaptive Learning and Monetary Policy Design," CEPR Discussion Papers 3962, C.E.P.R. Discussion Papers.
- Weber, Anke, 2007. "Heterogeneous expectations, learning and European inflation dynamics," Discussion Paper Series 1: Economic Studies 2007,16, Deutsche Bundesbank, Research Centre.
- Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
- Timothy Cogley & Argia M. Sbordone, 2006. "Trend inflation and inflation persistence in the New Keynesian Phillips curve," Staff Reports 270, Federal Reserve Bank of New York.
- Ricardo Nunes, 2005.
"Learning the inflation target,"
0504033, EconWPA, revised 26 Apr 2005.
- Evans, Martin & Wachtel, Paul, 1993. "Inflation Regimes and the," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(3), pages 475-511, August.
- Emiliano Santoro & Damjan Pfajfar, 2006. "Heterogeneity and learning in inflation expectation formation: an empirical assessment," Department of Economics Working Papers 0607, Department of Economics, University of Trento, Italia.
- Andolfatto, David & Scott Hendry & Kevin Moran, 2002.
"Inflation Expectations and Learning about Monetary Policy,"
Staff Working Papers
02-30, Bank of Canada.
- David Andolfatto & Scott Hendry & Kevin Moran, 2004. "Inflation Expectations and Learning about Monetary Policy," DNB Staff Reports (discontinued) 121, Netherlands Central Bank.
- Roberts, John M., 1997. "Is inflation sticky?," Journal of Monetary Economics, Elsevier, vol. 39(2), pages 173-196, July.
- Cukierman, Alex & Meltzer, Allan H, 1986. "A Theory of Ambiguity, Credibility, and Inflation under Discretion and Asymmetric Information," Econometrica, Econometric Society, vol. 54(5), pages 1099-1128, September.
- Christopher J. Erceg & Andrew T. Levin, 2001.
"Imperfect credibility and inflation persistence,"
Finance and Economics Discussion Series
2001-45, Board of Governors of the Federal Reserve System (U.S.).
- Branch, William A. & Evans, George W., 2006.
"A simple recursive forecasting model,"
Elsevier, vol. 91(2), pages 158-166, May.
- Wiliam Branch & George W. Evans, 2005. "A Simple Recursive Forecasting Model," University of Oregon Economics Department Working Papers 2005-3, University of Oregon Economics Department, revised 01 Feb 2005.
- William A. Branch, 2004. "The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations," Economic Journal, Royal Economic Society, vol. 114(497), pages 592-621, 07.
- Francis X. Diebold & Robert S. Mariano, 1994.
"Comparing Predictive Accuracy,"
NBER Technical Working Papers
0169, National Bureau of Economic Research, Inc.
- Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
- Cumby, Robert E & Huizinga, John, 1992.
"Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions,"
Econometric Society, vol. 60(1), pages 185-95, January.
- Robert E. Cumby & John Huizinga, 1990. "Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions," NBER Technical Working Papers 0092, National Bureau of Economic Research, Inc.
- Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388.
- Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-89, October.
When requesting a correction, please mention this item's handle: RePEc:ces:ifowps:_55. 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: (Klaus Wohlrabe)
If references are entirely missing, you can add them using this form.