A Grouped Factor Model
In this paper we present a grouped factor model that is designed to explore grouped structures in factor models. We develop an econometric theory consisting of a consistent classification rule to assign variables to their respective groups and a class of consistent model selection criteria to determine the number of groups as well as the number of factors in each group. As a result, we propose a procedure to estimate grouped factor models, in which the unknown number of groups, the unknown relationship between variables to their groups as well as the unknown number of factors in each group are statistically determined based on observed data. The procedure can help to estimate common factor that are pervasive across all groups and group-specific factors that are pervasive only in the respective groups. Simulations show that our proposed estimation procedure has satisfactory finite sample properties.
|Date of creation:||01 Oct 2010|
|Date of revision:||11 Jan 2011|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
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- Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics,
Elsevier, vol. 132(1), pages 169-194, May.
- Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
- Goyal, Amit & Pérignon, Christophe & Villa, Christophe, 2008. "How common are common return factors across the NYSE and Nasdaq?," Journal of Financial Economics, Elsevier, vol. 90(3), pages 252-271, December.
- Sydney C. Ludvigson & Serena Ng, 2009. "A Factor Analysis of Bond Risk Premia," NBER Working Papers 15188, National Bureau of Economic Research, Inc.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. Full references (including those not matched with items on IDEAS)
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