TY  - JOUR
AU  - West,Kenneth D.
AU  - Clark,Todd
TI  - Approximately Normal Tests for Equal Predictive Accuracy in Nested Models
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 326
PY  - 2006
Y2  - August 2006
UR  - http://www.nber.org/papers/t0326
L1  - http://www.nber.org/papers/t0326.pdf
N1  - Author contact info:
Kenneth D. West
Department of Economics
University of Wisconsin
1180 Observatory Drive
Madison, WI  53706
Tel: 608/262-0033
Fax: 608/262-2033
E-Mail: kdwest@wisc.edu

Todd Clark
Research Department
Federal Reserve Bank of Kansas City
1 Memorial Drive
Kansas City, MO  64198
Tel: 816 881 2575
E-Mail: todd.clark@clev.frb.org
AB  - Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model.  Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero.  We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model.  We describe how to adjust MSPEs to account for this noise.  We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero.  We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size.  Simulation evidence supports our recommended procedure.
ER  -