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Financial Condition Indices in an Incomplete Data Environment

Author

Listed:
  • Herculano Miguel C.

    (Adam Smith Business School, University of Glasgow, Glasgow, Scotland)

  • Jacob Punnoose

    (Reserve Bank of New Zealand, Wellington, New Zealand)

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.

Suggested Citation

  • Herculano Miguel C. & Jacob Punnoose, 2025. "Financial Condition Indices in an Incomplete Data Environment," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(1), pages 19-38.
    • Handle: RePEc:bpj:sndecm:v:29:y:2025:i:1:p:19-38:n:1002
    DOI: 10.1515/snde-2022-0115
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    More about this item

    Keywords

    financial conditions index; mixed-frequency; Bayesian methods;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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