Estimation of stable parameters for multiple autoregressive processes via convex programming
(2023) , 37 pages
Files
ISBA_DP_2023-37.pdf
Open Access - Adobe PDF
- 521.5 KB
Details
- Authors
- Abstract
- We develop a finite-sample theory for estimating the coefficients and for the prediction of multiple stable autoregressive processes that (i) share an unknown lag order but (ii) can differ in their individual sample sizes. Our technique is based on penalisation similar to hierarchical, overlapping group-Lasso but requires a new mathematical set-up to accommodate (i) and (ii). The set-up differs from existing work considerably, for example, in that we estimate the common lag order directly from the data rather than using extrinsic criteria. We prove that the estimated autoregressive processes enjoy stability, and we establish rates for both the estimation and prediction error that can outmatch the known rates in our setting. Our insights on the lag selection and the stability are also of interest for the case of individual autoregressive processes.
- Affiliations
Citations
Chakraborty, S., Lederer, J., & von Sachs, R. (2023). Estimation of stable parameters for multiple autoregressive processes via convex programming (LIDAM Discussion Paper ISBA 2023/37). https://hdl.handle.net/2078.5/27895