Financial Condition Indices in an Incomplete Data Environment
Miguel Herculano and
CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University
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)
Pages: 33 pages
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fdg
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2023-42
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