The asymptotic equivalence of ridge and principal component regression with many predictors
Subject
Economics
Publishing details
Authors / Editors
De Mol C;Giannone D;Reichlin L
Biographies
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
