A Sparse Kalman Filter: A Non-Recursive Approach

Michal Andrle and Jan Bruha

Working Papers from Czech National Bank, Research and Statistics Department

Abstract: We propose an algorithm to estimate unobserved states and shocks in a state-space model under sparsity constraints. Many economic models have a linear state-space form - for example, linearized DSGE models, VARs, time-varying VARs, and dynamic factor models. Under the conventional Kalman filter, which is essentially a recursive OLS algorithm, all estimated shocks are non-zero. However, the true shocks are often zero for multiple periods, and non-zero estimates are due to noisy data or ill-conditioning of the model. We show applications where sparsity is the natural solution. Sparsity of filtered shocks is achieved by applying an elastic-net penalty to the least-squares problem and improves statistical efficiency. The algorithm can be adapted for non-convex penalties and for estimates robust to outliers.

Keywords: Kalman filter; regularization; sparsity (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 (search for similar items in EconPapers)
Date: 2023-11
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cnb.cz/export/sites/cnb/en/economic-re ... wp/cnbwp_2023_13.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cnb:wpaper:2023/13

Access Statistics for this paper

More papers in Working Papers from Czech National Bank, Research and Statistics Department Contact information at EDIRC.
Bibliographic data for series maintained by Tomas Karhanek ().