On the real‐time predictive content of financial condition indices for growth

• Published in: Journal of applied econometrics (Chichester, England) Vol. 38; no. 2; pp. 137 - 163
• Main Authors: Amburgey, Aaron J., McCracken, Michael W.
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Periodicals Inc 01.03.2023
• Subjects: Asymptotic methods; Econometrics; GDP; Gross Domestic Product; out‐of‐sample forecasts; Policy making; Quantiles; real‐time data; Regression models
• ISSN: 0883-7252; 1099-1255
• DOI: 10.1002/jae.2943

Summary We provide evidence on the real‐time predictive content of the National Financial Conditions Index (NFCI), for conditional quantiles of U.S. real GDP growth. Our work is distinct from the literature in two specific ways. First, we construct (unofficial) real‐time vintages of the NFCI. This allows us to conduct out‐of‐sample analysis without introducing the kind of look‐ahead biases that are naturally introduced when using a single current vintage. We then develop methods for conducting asymptotic inference on tests of equal tick loss between nested quantile regression models when the data are subject to revision. We conclude by evaluating the real‐time predictive content of NFCI vintages for quantiles of real GDP growth. While our results largely reinforce the literature, we find gains to using real‐time vintages leading up to recessions—precisely when policymakers need such a monitoring device.