Are More Data Always Better for Factor Analysis?

27 Pages Posted: 14 Jul 2003  

Jean Boivin

HEC Montreal; Columbia Business School; National Bureau of Economic Research (NBER)

Serena Ng

Columbia Business School - Economics Department

Date Written: July 2003

Abstract

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.

Suggested Citation

Boivin, Jean and Ng, Serena, Are More Data Always Better for Factor Analysis? (July 2003). NBER Working Paper No. w9829. Available at SSRN: https://ssrn.com/abstract=423302

Jean Boivin (Contact Author)

HEC Montreal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada

Columbia Business School ( email )

3022 Broadway
Department of Economics and Finance Room 719, Uris Hall
New York, NY 10027
United States
212-854-9091 (Phone)
212-316-9355 (Fax)

HOME PAGE: http://www.columbia.edu/~jb903/

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Serena Ng

Columbia Business School - Economics Department ( email )

420 West 118th Street
New York, NY 10027
United States

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