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An inflation expectations horserace

  • Guzman, Giselle C.

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 Survey. While these measures have been useful in developing models of forecasting inflation, the data are low frequency measures which appear 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 36511.

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Date of creation: 25 Jan 2010
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Handle: RePEc:pra:mprapa:36511
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  1. 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.
  2. N. Gregory Mankiw & Ricardo Reis, 2001. "Sticky Information Versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," Harvard Institute of Economic Research Working Papers 1922, Harvard - Institute of Economic Research.
  3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  4. 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.
  5. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  6. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
  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. Andrew Ang & Geert Bekaert & Min Wei, 2006. "Do macro variables, asset markets, or surveys forecast inflation better?," Finance and Economics Discussion Series 2006-15, Board of Governors of the Federal Reserve System (U.S.).
  9. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
  10. Mariano, Roberto S. & Preve, Daniel, 2012. "Statistical tests for multiple forecast comparison," Journal of Econometrics, Elsevier, vol. 169(1), pages 123-130.
  11. 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.
  12. Guzman, Giselle C., 2008. "Using sentiment surveys to predict GDP growth and stock returns," MPRA Paper 36653, University Library of Munich, Germany.
  13. Grant, Alan P. & Thomas, Lloyd B., 1999. "Inflationary expectations and rationality revisited," Economics Letters, Elsevier, vol. 62(3), pages 331-338, March.
  14. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
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