Generalised Canonical Correlation Estimation of the Multilevel Factor Model
We develop a novel approach based on the generalised canonical correlation (GCC) analysis to consistently estimating the multilevel factor model and providing the proper inference theory for the high dimensional panel data. Importantly, our approach is shown to be robust to a non-zero correlation between the local factors across the different blocks and valid even if some blocks share the same local factors. %Moreover, our approach achieves consistency without iteration and is easy to implement. We also propose a novel selection criterion for identifying the number of the global factors. Relevant asymptotic theories %normal distributions of the estimated factors and loadings are derived under fairly standard conditions. Via Monte Carlo simulations, we show the satisfactory and dominant performance of the $GCC$ estimator relative to existing approaches. Finally, we demonstrate its usefulness with an application to the housing market in England and Wales using a large disaggregated panel data of the real house price growth rates for the 331 local authorities over the period 1996Q1 to 2021Q2
Year of publication: |
2022
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Authors: | Lin, Rui ; Shin, Yongcheol |
Publisher: |
[S.l.] : SSRN |
Subject: | Schätzung | Estimation | Korrelation | Correlation | Schätztheorie | Estimation theory | Faktorenanalyse | Factor analysis |
Saved in:
freely available
Extent: | 1 Online-Ressource (67 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 6, 2022 erstellt |
Other identifiers: | 10.2139/ssrn.4295429 [DOI] |
Classification: | c55 ; R31 - Housing Supply and Markets |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014237949
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