Working Paper

On the Real-Time Predictive Content of Financial Conditions Indices for Growth

Abstract: 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.

Keywords: out-of-sample forecasts; real-time data; quantiles;

JEL Classification: C12; C32; C38; C52;

https://doi.org/10.20955/wp.2022.003

Status: Published in Journal of Applied Econometrics

Bibliographic Information

Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2022-01-18

Number: 2022-003

Note: Publisher DOI: https://doi.org/10.1002/jae.2943

Related Works

• Working Paper Revision: On the Real-Time Predictive Content of Financial Conditions Indices for Growth