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A least-squares filter for sequence-space models

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  • Rigato, Rodolfo

Abstract

This note introduces an algorithm for efficiently filtering unobserved shocks in linear sequence-space models. It solves a least-squares optimization problem in closed form that returns the expectation of a vector 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.

Suggested Citation

  • Rigato, Rodolfo, 2026. "A least-squares filter for sequence-space models," Economics Letters, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:ecolet:v:265:y:2026:i:c:s0165176526001825
    DOI: 10.1016/j.econlet.2026.112988
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