A randomised sequential procedure to determine the number of factors
This paper proposes a procedure to estimate the number of common factors k in a static approximate factor model. The building block of the analysis is the fact that the first k eigenvalues of the covariance matrix of the data diverge, whilst the others stay bounded. On the grounds of this, we propos...
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| Format: | Article |
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Taylor & Francis
2017
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| Online Access: | https://eprints.nottingham.ac.uk/46952/ |
| _version_ | 1848797435448524800 |
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| author | Trapani, Lorenzo |
| author_facet | Trapani, Lorenzo |
| author_sort | Trapani, Lorenzo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper proposes a procedure to estimate the number of common factors k in a static approximate factor model. The building block of the analysis is the fact that the first k eigenvalues of the covariance matrix of the data diverge, whilst the others stay bounded. On the grounds of this, we propose a test for the null that the i-th eigenvalue diverges, using a randomised test statistic based directly on the estimated eigenvalue. The test only requires minimal assumptions on the data, and no assumptions are required on factors, loadings or idiosyncratic errors. The randomised tests are then employed in a sequential procedure to determine k. |
| first_indexed | 2025-11-14T20:03:50Z |
| format | Article |
| id | nottingham-46952 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:03:50Z |
| publishDate | 2017 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-469522020-05-04T18:51:59Z https://eprints.nottingham.ac.uk/46952/ A randomised sequential procedure to determine the number of factors Trapani, Lorenzo This paper proposes a procedure to estimate the number of common factors k in a static approximate factor model. The building block of the analysis is the fact that the first k eigenvalues of the covariance matrix of the data diverge, whilst the others stay bounded. On the grounds of this, we propose a test for the null that the i-th eigenvalue diverges, using a randomised test statistic based directly on the estimated eigenvalue. The test only requires minimal assumptions on the data, and no assumptions are required on factors, loadings or idiosyncratic errors. The randomised tests are then employed in a sequential procedure to determine k. Taylor & Francis 2017-06-26 Article PeerReviewed Trapani, Lorenzo (2017) A randomised sequential procedure to determine the number of factors. Journal of the American Statistical Association . ISSN 1537-274X Approximate factor models Randomised tests Number of factors https://doi.org/10.1080/01621459.2017.1328359 doi:10.1080/01621459.2017.1328359 doi:10.1080/01621459.2017.1328359 |
| spellingShingle | Approximate factor models Randomised tests Number of factors Trapani, Lorenzo A randomised sequential procedure to determine the number of factors |
| title | A randomised sequential procedure to determine the number of factors |
| title_full | A randomised sequential procedure to determine the number of factors |
| title_fullStr | A randomised sequential procedure to determine the number of factors |
| title_full_unstemmed | A randomised sequential procedure to determine the number of factors |
| title_short | A randomised sequential procedure to determine the number of factors |
| title_sort | randomised sequential procedure to determine the number of factors |
| topic | Approximate factor models Randomised tests Number of factors |
| url | https://eprints.nottingham.ac.uk/46952/ https://eprints.nottingham.ac.uk/46952/ https://eprints.nottingham.ac.uk/46952/ |