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Abstract

In this paper we present a grouped factor model that is designed to explore clustering structures in large factor models. We develop a procedure that will endogenously assign variables to groups, determine the number of groups, and estimate common factors for each group. The grouped factor model provides not only an alternative way to factor rotations in discovering orthogonal and non-orthogonal clusterings in a factor space. It offers also an effective method to explore more general clustering structures in a factor space which are invisible by factor rotations: the factor space can consist of subspaces of variousdimensions that may be disjunct, orthogonal, or intersected with any angels. Hence a grouped factor model may provide a more detailed insight into data and thus also more understandable and interpretable factors.



Item Type: MPRA Paper -

Original Title: A grouped factor model-

English Title: A Grouped Factor Model-

Language: English-

Keywords: Factor Models; Generalized Principal Component Analysis; Model Selection-

Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation ModelingC - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes-





Autor: Chen, Pu

Fuente: https://mpra.ub.uni-muenchen.de/34075/







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