Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319273 
Year of Publication: 
2024
Citation: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 46 [Issue:] 2 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2024 [Pages:] 235-257
Publisher: 
John Wiley & Sons, Ltd, Oxford, UK
Abstract: 
Ridge regression is a popular method for dense least squares regularization. In this article, ridge regression is studied in the context of VAR model estimation and inference. The implications of anisotropic penalization are discussed, and a comparison is made with Bayesian ridge‐type estimators. The asymptotic distribution and the properties of cross‐validation techniques are analyzed. Finally, the estimation of impulse response functions is evaluated with Monte Carlo simulations and ridge regression is compared with a number of similar and competing methods.
Subjects: 
Impulse responses
inference
ridge regularization
vector autoregression
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article
Document Version: 
Published Version
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