Financial Condition Indices in an Incomplete Data Environment

Herculano Miguel C. () and Jacob Punnoose ()
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Herculano Miguel C.: Adam Smith Business School, University of Glasgow, Glasgow, Scotland
Jacob Punnoose: Reserve Bank of New Zealand, Wellington, New Zealand

Studies in Nonlinear Dynamics & Econometrics, 2025, vol. 29, issue 1, 19-38

Abstract: We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.

Keywords: financial conditions index; mixed-frequency; Bayesian methods (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 C53 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/snde-2022-0115

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