Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach

We develop an unobserved-components approach to study surveys of forecasts containing multiple forecast horizons. Under the assumption that forecasters optimally update their beliefs about past, current, and future state variables as new information arrives, we use our model to extract information on the degree of predictability of the state variable and the importance of measurement errors in the observables. Empirical estimates of the model are obtained using survey forecasts of annual GDP growth and inflation in the United States with forecast horizons ranging from 1 to 24 months, and the model is found to closely match the joint realization of forecast errors at different horizons. Our empirical results suggest that professional forecasters face severe measurement error problems for GDP growth in real time, while this is much less of a problem for inflation. Moreover, inflation exhibits greater persistence, and thus is predictable at longer horizons, than GDP growth and the persistent component of both variables is well approximated by a low-order autoregressive specification.

[1]  Steve Swidler,et al.  Economic Forecasts, Rationality, and the Processing of New Information over Time , 1990 .

[3]  Christopher D. Carroll,et al.  Macroeconomic Expectations of Households and Professional Forecasters , 2003 .

[4]  Anthony Garratt,et al.  Forecast Uncertainties in Macroeconometric Modelling: An Application to the UK Economy , 2000, SSRN Electronic Journal.

[5]  Francis X. Diebold,et al.  Real-Time Measurement of Business Conditions , 2007 .

[6]  Min Wei,et al.  Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? , 2005 .

[7]  Stephen Taylor,et al.  Forecasting Economic Time Series , 1979 .

[8]  C. Granger,et al.  Forecasting and Decision Theory , 2006 .

[9]  Norman R. Swanson,et al.  Information in the Revision Process of Real-Time Datasets , 2008 .

[10]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

[11]  William D. Nordhaus,et al.  Forecasting Efficiency: Concepts and Applications , 1987 .

[12]  James D. Hamilton.  Time Series Analysis , 1994 .

[13]  Andrew J. Patton,et al.  Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion , 2010 .

[14]  J. Stock,et al.  Macroeconomic Forecasting Using Diffusion Indexes , 2002 .

[15]  P. Sørensen,et al.  The Strategy of Professional Forecasting , 2001 .

[16]  J. Stock,et al.  Forecasting with Many Predictors , 2006 .

[17]  Michael P. Clements.  Evaluating the Rationality of Fixed-Event Forecasts , 1997 .

[18]  L. Hansen.  Large Sample Properties of Generalized Method of Moments Estimators , 1982 .

[19]  David H. Small,et al.  Nowcasting: the real time informational content of macroeconomic data releases , 2008 .

[20]  Lavan Mahadeva,et al.  National Accounts Revisions and Output Gap Estimates in a Model of Monetary Policy with Data Uncertainty , 2005 .

[21]  Jonathan H. Wright,et al.  Federal Reserve Board , 2000 .

[22]  George Tauchen,et al.  Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained From Financial Market Data , 1986 .

[23]  Kajal Lahiri,et al.  How quickly do forecasters incorporate news? Evidence from cross-country surveys , 2006 .

[24]  Glenn D. Rudebusch,et al.  Forecasting Output with the Composite Leading Index: A Real-Time Analysis , 1991 .

[25]  David I. Harvey.  The evaluation of economic forecasts , 1997 .

[26]  Dean Croushore,et al.  Forecasting with Real-Time Macroeconomic Data , 2006 .

[27]  Dean Croushore,et al.  A real-time data set for macroeconomists , 2001 .

[28]  K. Lahiri,et al.  Evolution of Forecast Disagreement in a Bayesian Learning Model , 2007 .

[29]  Anthony Davies,et al.  A new framework for analyzing survey forecasts using three-dimensional panel data☆ , 1995 .

[30]  Andrew J. Patton,et al.  Testing Forecast Optimality Under Unknown Loss , 2007 .

[31]  Anthony Garratt,et al.  Forecast Uncertainties in Macroeconomic Modeling , 2003 .