Forecasting with a Panel Tobit Model

TL;DR

This paper introduces a Bayesian dynamic panel Tobit model with heteroskedasticity for forecasting censored time series data, demonstrating its application in predicting bank loan charge-off rates.

Contribution

It develops a fully Bayesian approach to estimate heterogeneous coefficients and construct density and set forecasts for large cross-sections of censored data.

Findings

Effective in forecasting bank charge-off rates

Provides both density and set forecasts with coverage guarantees

Handles heteroskedasticity in panel data

Abstract

We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. In addition to density forecasts, we construct set forecasts that explicitly target the average coverage probability for the cross-section. We present a novel application in which we forecast bank-level loan charge-off rates for small banks.