TY  - JOUR
AU  - Boivin,Jean
AU  - Ng,Serena
TI  - Are More Data Always Better for Factor Analysis?
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 9829
PY  - 2003
Y2  - July 2003
UR  - http://www.nber.org/papers/w9829
L1  - http://www.nber.org/papers/w9829.pdf
N1  - Author contact info:
Jean Boivin
Bank of Canada
234 Wellington Street
Ottawa
Ontario K1A 0G9
Canada
Tel: 613-782-8278
E-Mail: jboivin@bankofcanada.ca

Serena Ng
Department of Economics
Columbia University
440 W. 118 St.
International Affairs Building, MC 3308
New York
NY 10027
Tel: 212-854-5488
E-Mail: serena.ng@columbia.edu
AB  - Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors, and yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can also arise if forecasting power is provided by a factor that is dominant in a small dataset but is a dominated factor in a larger dataset. In a real time forecasting exercise, we find that factors extracted from as few as 40 pre-screened series often yield satisfactory or even better results than using all 147 series. Weighting the data by their properties when constructing the factors also lead to improved forecasts. Our simulation analysis is unique in that special attention is paid to cross-correlated idiosyncratic errors, and we also allow the factors to have stronger loadings on some groups of series than others. It thus allows us to better understand the properties of the principal components estimator in empirical applications.
ER  -