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- W3204161730 abstract "We develop a novel approach based on the canonical correlation analysis to identify the number of global factors in the multilevel factor model. We propose the two consistent selection criteria, the canonical correlations difference (CCD) and the modified canonical correlations (MCC). Via Monte Carlo simulations, we show that CCD and MCC select the number of global factors correctly even in small samples, and they are robust to the presence of serially correlated and weakly cross-sectionally correlated idiosyncratic errors as well as the correlated local factors. Finally, we demonstrate the utility of our approach with an application to the multilevel asset pricing model for the stock return data in 12 industries in the U.S." @default.
- W3204161730 created "2021-10-11" @default.
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- W3204161730 date "2020-01-01" @default.
- W3204161730 modified "2023-10-16" @default.
- W3204161730 title "Canonical Correlation-based Model Selection for the Multilevel Factors" @default.
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- W3204161730 doi "https://doi.org/10.2139/ssrn.3590109" @default.
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