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US Consumer Inflation Expectations: Evidence Regarding Learning, Accuracy and Demographics

  • Robert D. J. Anderson

Central banks have become increasingly aware of the importance of consumer inflation expectations in meeting monetary policy objectives. US consumer year-ahead inflation expectations data is available as measured by the Michigan 'Survey of Consumer Attitudes and Behavior'. Using the detailed demographic information recorded as part of the interview process to accommodate forecast heterogeneity, results suggest the accuracy of forecasts is linked to the demographic characteristics of the respondent. This survey also contains a short-rotating panel dimension, with most respondents being reinterviewed six months after the initial interview. Uniquely, this paper uses these matched interviews to examine whether consumers learn about inflation, improving the accuracy of their forecast from initial to reinterview. Results suggest, having corrected for attrition bias, that being reinterviewed stimulates agents to learn and improve forecast accuracy, the level of improvement being dependent on the demographic characteristic of the interviewee.

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File URL: http://www.socialsciences.manchester.ac.uk/medialibrary/cgbcr/discussionpapers/dpcgbcr99.pdf
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Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 99.

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Length: 41 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:man:cgbcrp:99
Contact details of provider: Postal: Manchester M13 9PL
Phone: (0)161 275 4868
Fax: (0)161 275 4812
Web page: http://www.socialsciences.manchester.ac.uk/subjects/economics/our-research/centre-for-growth-and-business-cycle-research/

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  1. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition and stratification," CeMMAP working papers CWP11/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584.
  3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  5. repec:dgr:kubcen:2005131 is not listed on IDEAS
  6. Souleles, Nicholas S, 2004. "Expectations, Heterogeneous Forecast Errors, and Consumption: Micro Evidence from the Michigan Consumer Sentiment Surveys," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(1), pages 39-72, February.
  7. Inkmann, J., 2005. "Inverse Probability Weighted Generalised Empirical Likelihood Estimators : Firm Size and R&D Revisited," Discussion Paper 2005-131, Tilburg University, Center for Economic Research.
  8. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
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