Skip to main content

Please enter a keyword and click the arrow to search the site

The asymptotic equivalence of ridge and principal component regression with many predictors

Subject

Economics

Authors / Editors

De Mol C;Giannone D;Reichlin L

Publication Year

2024

Abstract

The asymptotic properties of ridge regression in large dimension are studied. Two key results are established. First, consistency and rates of convergence for ridge regression are obtained under assumptions which impose different rates of increase in the dimension n between the first n1 and the remaining n-n1 eigenvalues of the population covariance of the predictors. Second, it is proved that under the special and more restrictive case of an approximate factor structure, principal component and ridge regression have the same rate of convergence and the rate is faster than the one previously established for ridge.

Keywords

High-dimensional time series; Factor models; Forecasting; Ridge regression; Asymptotic inference

Available on ECCH

No


Select up to 4 programmes to compare

Select one more to compare
×
subscribe_image_desktop 5949B9BFE33243D782D1C7A17E3345D0

Sign up to receive our latest news and business thinking direct to your inbox

×

Sign up to receive our latest course information and business thinking

Leave your details above if you would like to receive emails containing the latest thought leadership, invitations to events and news about courses that could enhance your career. If you would prefer not to receive our emails, you can still access the case study by clicking the button below. You can opt-out of receiving our emails at any time by visiting: https://london.edu/my-profile-preferences or by unsubscribing through the link provided in our emails. View our Privacy Policy for more information on your rights.