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Automatic Lag Selection in Covariance Matrix Estimation
Abstract
We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.
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Citations
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Persyn, D., & Westerlund, J. (2008) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800800205 · Cited by 770
Climate Change Concerns and the Performance of Green vs. Brown Stocks
Ardia, D., Bluteau, K., Boudt, K., & Inghelbrecht, K. (2023) · Management Science · doi:10.1287/mnsc.2022.4636 · Cited by 639
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Metadata
- Authors
- Newey, W. K., & West, K. D.
- Venue
- The Review of Economic Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 1994-10-01
- Volume
- 61
- Issue
- 4
- Pages
- 631-653
- ISSN
- 0034-6527 (ISSN-L)
- 1467-937X
- OpenAlex
- W3124550551
- Cited by
- 3,235
- References
- 0
- FWCI
- 34.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Newey, W. K., & West, K. D. (1994). Automatic Lag Selection in Covariance Matrix Estimation. The Review of Economic Studies. https://doi.org/10.2307/2297912
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