A least-squares filter for sequence-space models

Rodolfo Dinis Rigato

No 3191, Working Paper Series from European Central Bank

Abstract: Sequence-space models are becoming increasingly popular in macroeconomics, especially in the heterogeneous-agent literature. However, the econometric toolkit for users of these models remains less developed than that available for traditional state-space methods. This note introduces an algorithm for efficiently filtering unobserved shocks in linear sequence-space models. The proposed filter solves a least-squares optimization problem in closed form and returns the expectation of unobserved shocks conditional on observed data. It handles heteroskedasticity, missing observations, measurement error, and non- Gaussian shock distributions. To illustrate its properties, I apply it to data simulated from a medium-scale heterogeneous-agent New Keynesian model and show that it accurately recovers the underlying structural shocks. JEL Classification: C32, E27, E32, E37

Keywords: filtering; least squares; sequence space (search for similar items in EconPapers)
Date: 2026-02
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20263191

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