Guzman, Giselle C. (2009): An inflation expectations horserace.
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Abstract
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 realtime data. I present a collection of 37 different measures of inflation expectations, including many previously unexploited monthly and realtime 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.
Item Type:  MPRA Paper 

Original Title:  An inflation expectations horserace 
Language:  English 
Keywords:  Inflation; expectations; surveys; households; economists; rationality; efficiency; unbiasedness; forecast accuracy; outofsample forecasts; Granger Causality; highfrequency data; price level; money and prices; CPI; PPI; PCE 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E47  Forecasting and Simulation: Models and Applications D  Microeconomics > D8  Information, Knowledge, and Uncertainty > D84  Expectations ; Speculations E  Macroeconomics and Monetary Economics > E2  Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E24  Employment ; Unemployment ; Wages ; Intergenerational Income Distribution ; Aggregate Human Capital ; Aggregate Labor Productivity C  Mathematical and Quantitative Methods > C0  General > C02  Mathematical Methods G  Financial Economics > G1  General Financial Markets > G14  Information and Market Efficiency ; Event Studies ; Insider Trading C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C82  Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E31  Price Level ; Inflation ; Deflation E  Macroeconomics and Monetary Economics > E5  Monetary Policy, Central Banking, and the Supply of Money and Credit > E51  Money Supply ; Credit ; Money Multipliers E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E44  Financial Markets and the Macroeconomy C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General D  Microeconomics > D0  General > D03  Behavioral Microeconomics: Underlying Principles E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E32  Business Fluctuations ; Cycles C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods ?? C42 ?? E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E37  Forecasting and Simulation: Models and Applications D  Microeconomics > D0  General > D01  Microeconomic Behavior: Underlying Principles C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  40233 
Depositing User:  Giselle Guzman 
Date Deposited:  24 Jul 2012 03:48 
Last Modified:  07 Feb 2016 15:01 
References:  Stiglitz, Joseph (2011). "Rethinking Macroeconomics: What Failed, and How to Repair It." Journal of the European Economic Association, Volume 9, Issue 4, pp. 591645, August 2011. Stiglitz, Joseph (2011). "An Agenda for Reforming Economic Theory." http://economistsview.typepad.com/files/session3stiglitz_agenda_for_reform.pdf Ang, A., Bekaert, G., and Wei, M. (2007). Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics, 54 (2007), pp. 11631212. Calvo, G. (1983). Staggered Prices in a UtilityMaximizing Framework. Journal of Monetary Economics, 12 (1983), pp. 383398. Carroll, C. (2003). Macroeconomic Expectations of Households and Professional Forecasters. The Quarterly Journal of Economics, Vol. 118, No. 1 (Feb. 2003), pp. 269298. Engle, Robert F., and Granger, C.W.J. (1987). CoIntegration and Error Correction: Representation, Estimation, and Testing. Econometrica, Vol. 55, March, pp. 251276. Giannone, D., Reichlin, L., and Small, D. (2008). Nowcasting: The realtime informational content of macroeconomic data. Journal of Monetary Economics, 55 (2008), pp. 665676. Gramlich, E.M. (1983). Models of Inflation Expectations Formation: A Comparison of Household and Economist Forecasts. Journal of Money, Credit, and Banking, Vol. 15, No. 2, May 1983, pp. 155173. Granger, C.W.J., and Newbold, P. (1977). Forecasting Economic Time Series, Orlando, FL. Academic Press. Grant, A.P. and Thomas, L.B. (1999). Inflationary expectations and rationality revisited. Economics Letters, Vol. 62, pp. 331338. Guzmán, Giselle (2003). GDP Growth Signals, Investor Psychology, and Hedging Pressure: A Multivariate Approach to Forecasting Returns on the S&P500 Index. Manuscript, The Wharton School, University of Pennsylvania, April, 2003. Guzmán, Giselle (2008). Using Sentiment Surveys to Predict GDP Growth and Stock Returns. The Making of National Economic Forecasts, edited by Lawrence R. Klein. Edward Elgar Publishing, Ltd. London, 2009. Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13, (1997), pp. 281291. Lanne, M., Luoma, A. and Luoto, J. (2009). A naïve sticky information model of households’ inflation expectations. Journal of Economic Dynamics & Control, 33 (2009), pp. 13321344. Mankiw, G. and Reis, R. (2002). Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve. The Quarterly Journal of Economics, Vol. 117, No. 4 (Nov. 2002), pp. 12951328. Mariano, R. and Preve, D. (2008). Statistical Tests for Multiple Forecast Comparison. Presentation, T.W. Anderson Conference, Stanford University, 2008. Mehra, Y.P. (2002). Survey Measures of Expected Inflation: Revisiting the Issues of Predictive Content and Rationality. Federal Reserve Bank of Richmond Economic Quarterly, Vol. 88/3, Summer 2002, pp. 1735. Morgan, W.A. (1939). A Test for Significance of the Difference Between the Two Variances in a Sample From a Normal Bivariate Population. Biometrika, 31, 1319. Thomas, L.B. (1999). Survey Measures of Expected U.S. Inflation. The Journal of Economic Perspectives, Vol. 13, No. 4, Autumn 1999, pp. 125144. Stock, J.H. and Watson, M.W. (1999). Forecasting inflation. Journal of Monetary Economics, 44 (1999), pp. 293335. Stock, J.H. and Watson, M.W. (2002). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business & Economic Statistics. Vol. 20, No. 2 (April 2002), pp. 147162. Woodford, M. (2003). Interest and Prices. Princeton University Press, Princeton, NJ. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/40233 
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