TY  - JOUR
AU  - Faust,Jon
AU  - Wright,Jonathan H.
TI  - Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 13397
PY  - 2007
Y2  - September 2007
UR  - http://www.nber.org/papers/w13397
L1  - http://www.nber.org/papers/w13397.pdf
N1  - Author contact info:
Jon Faust
Johns Hopkins University
Department of Economics
Mergenthaler Hall 456
3400 N. Charles Street
Baltimore, MD 25218
Tel: 410/516-7614
Fax: 410/516-7600
E-Mail: faustj@jhu.edu

Jonathan Wright
Johns Hopkins University
E-Mail: wrightj@jhu.edu
AB  - Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare real-time large dataset predictions to both simple univariate methods and  to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast.
ER  -