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The case for higher frequency inflation expectations

  • Guzman, Giselle C.

I present evidence that higher frequency measures of inflation expectations outperform lower frequency measures of inflation expectations in tests of accuracy, predictive power, and rationality. For decades, the academic literature has focused on three survey measures of expected inflation: the Livingston Survey, the Survey of Professional Forecasters, and the Michigan Surveys of Consumers. While these measures have been useful in developing models of forecasting inflation, the data are low frequency measures that are anachronistic in the modern era of high frequency and real-time data. I present a collection of 37 different measures of inflation expectations, including many previously unexploited monthly and real-time measures of inflation expectations. These higher frequency measures tend to outperform the standard three low frequency survey measures in tests of accuracy, predictive power, and rationality, indicating that there are benefits to using higher frequency measures of inflation expectations. Out of sample forecasts confirm the findings.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 36656.

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Date of creation: 29 Jun 2011
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Handle: RePEc:pra:mprapa:36656
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  1. Mankiw, N. Gregory & Reis, Ricardo, 2002. "Sticky Information Versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," Scholarly Articles 3415324, Harvard University Department of Economics.
  2. Gramlich, Edward M, 1983. "Models of Inflation Expectations Formation: A Comparison of Household and Economist Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(2), pages 155-73, May.
  3. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
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  6. Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 0633, European Central Bank.
  7. Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "A naïve sticky information model of households' inflation expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 33(6), pages 1332-1344, June.
  8. Joseph E. Stiglitz, 2011. "Rethinking Macroeconomics: What Failed, And How To Repair It," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 591-645, 08.
  9. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  10. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  11. Mariano, Roberto S. & Preve, Daniel, 2012. "Statistical tests for multiple forecast comparison," Journal of Econometrics, Elsevier, vol. 169(1), pages 123-130.
  12. 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.
  13. Guzman, Giselle C., 2008. "Using sentiment surveys to predict GDP growth and stock returns," MPRA Paper 36653, University Library of Munich, Germany.
  14. repec:pra:mprapa:36512 is not listed on IDEAS
  15. Grant, Alan P. & Thomas, Lloyd B., 1999. "Inflationary expectations and rationality revisited," Economics Letters, Elsevier, vol. 62(3), pages 331-338, March.
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