The Formation of Inflation Expectations under Changing Inflation Regimes
The present article offers a careful description of empirical identification of possible multiple changes in regime. We apply recently developed tools designed to select among regime-switching models among a broad class of linear and nonlinear regression models and provide a discussion of the impact on the formation of inflation expectations in the presence of multiple and recurrent changes in inflation regimes. Our empirical findings give a plausible explanation as to why the rational-expectations hypothesis based on direct measures of inflation expectations from survey series is typically rejected because of large systematic differences between actual and expected inflation rates. In particular, our results indicate that in the case of changing and not perfectly observed inflation regimes, inference about rationality and unbiasedness based on a comparison of ex ante forecasts from survey series and actual inflation rate based on ex post realizations will be ambiguous because of the presence of an ex post bias. The empirical findings are based on Danish inflation rates covering 1957-1998. We show that it is not possible to reject the hypothesis of multiple inflationary regimes and that the actual inflation rate can be represented by a two-state Markov regime-switching model. It turns out that the real-time forecasts produced from this model exhibit a large degree of similarity when compared to the direct measures of inflation expectations. The result illustrates the important impact of switching regimes on the formation of actual and expected inflation and hence of ex post bias as a main contributor to the difference between actual and expected inflation observed directly from survey series.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 4 (2001)
Issue (Month): 4 (January)
|Contact details of provider:|| Web page: https://www.degruyter.com|
|Order Information:||Web: https://www.degruyter.com/view/j/snde|
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.:
- Hamilton, James D, 2001.
"A Parametric Approach to Flexible Nonlinear Inference,"
Econometric Society, vol. 69(3), pages 537-573, May.
- Hamilton, James D., 1999. "A Parametric Approach to Flexible Nonlinear Inference," University of California at San Diego, Economics Working Paper Series qt68s8157x, Department of Economics, UC San Diego.
- Hansen, Bruce E, 1997. "Approximate Asymptotic P Values for Structural-Change Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 60-67, January.
- Bruce E. Hansen, 1995. "Approximate Asymptotic P-Values for Structural Change Tests," Boston College Working Papers in Economics 297., Boston College Department of Economics.
- Hansen, Bruce E, 1996. "Erratum: The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 195-198, March-Apr.
- Bruce E. Hansen, 1995. "Erratum: The Likelihood ratio Test Under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Boston College Working Papers in Economics 296., Boston College Department of Economics.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
- Baillie, Richard T & Chung, Ching-Fan & Tieslau, Margie A, 1996. "Analysing Inflation by the Fractionally Integrated ARFIMA-GARCH Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(1), pages 23-40, Jan.-Feb.. Full references (including those not matched with items on IDEAS)