The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Large Dimensional Factor Analysis
Open accessAbstract
Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or dynamic factor models that can be cast in a static framework. Among the topics covered are how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy of observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models. The fundamental result that justifies these analyses is that the method of asymptotic principal components consistently estimates the true factor space. We use simulations to better understand the conditions that can affect the precision of the factor estimates.
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Citations
Cites
Co-integration and error correction: representation, estimation and testing
Engle, R. F., & Granger, C. W. J. (1987) · Econometrica · doi:10.2307/1913236 · Cited by 32,007
A simple panel unit root test in the presence of cross‐section dependence
Pesaran, M. H. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.951 · Cited by 12,787
Cited by
Onatski, A. (2012) · Journal of Econometrics · doi:10.1016/j.jeconom.2012.01.034 · Cited by 314
Metadata
- Authors
- Bai, J., & Ng, S.
- Venue
- Foundations and Trends in Econometrics
- Publisher
- Emerald
- Published
- 2008-05-06
- Volume
- 3
- Issue
- 2
- Pages
- 89-163
- ISSN
- 1551-3076 (ISSN-L)
- 1551-3084
- OpenAlex
- W2170202041
- Cited by
- 410
- References
- 2
- FWCI
- 19.72
- Type
- journal article
- Language
- en
- Open access
- diamond
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
