Nowcasting distributions: a functional MIDAS model

TL;DR

This paper introduces a functional MIDAS model that utilizes high-frequency data and functional principal component analysis to improve the accuracy of nowcasting the entire income distribution and its key features, aiding inequality analysis.

Contribution

The paper develops a novel functional MIDAS approach with a group lasso spike-and-slab prior for predictor selection, enhancing distributional forecasting accuracy.

Findings

Improved forecast accuracy for the income distribution

Enhanced prediction of inequality-related features

Effective use of high-frequency data in distributional nowcasting

Abstract

We propose a functional MIDAS model to leverage high-frequency information for forecasting and nowcasting distributions observed at a lower frequency. We approximate the low-frequency distribution using Functional Principal Component Analysis and consider a group lasso spike-and-slab prior to identify the relevant predictors in the finite-dimensional SUR-MIDAS approximation of the functional MIDAS model. In our application, we use the model to nowcast the U.S. households' income distribution. Our findings indicate that the model enhances forecast accuracy for the entire target distribution and for key features of the distribution that signal changes in inequality.