nep-for New Economics Papers
on Forecasting
Issue of 2023‒07‒24
two papers chosen by
Rob J Hyndman
Monash University

  1. What Charge-Off Rates Are Predictable by Macroeconomic Latent Factors? By Hyeongwoo Kim; Jisoo Son
  2. Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity By Kohns, David; Potjagailo, Galina

  1. By: Hyeongwoo Kim; Jisoo Son
    Abstract: Charge-offs signal critical information regarding the risk level of loan portfolios in the banking system, and they indicate the potential for systemic risk towards deep recessions. Utilizing consolidated financial statements, we have compiled the net charge-off rate (COR) data from the 10 largest U.S. bank holding companies (BHCs) for disaggregated loans, including business loans, real estate loans, and consumer loans, as well as the average top 10 COR for each loan categoy. We propose factor-augmented forecasting models for CORs that incorporate latent common factor estimates, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models have demonstrated superior performance compared with benchmark forecasting models especially well for business loan and real estate loan CORs, while predicting consumer loan CORs remains challenging especially at short horizons. Notably, real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.
    Keywords: Net Charge-Off Rate; Top 10 Bank Holding Companies; Disaggregated Loan CORs; Principal Component Analysis; Partial Least Squares; Out-of-Sample Forecast
    JEL: C38 C53 C55 G01 G17
    Date: 2023–07
  2. By: Kohns, David (Aalto University); Potjagailo, Galina (Bank of England)
    Abstract: We propose a mixed‑frequency regression prediction approach that models a time‑varying trend, stochastic volatility and fat tails in the variable of interest. The coefficients of high‑frequency indicators are regularised via a shrinkage prior that accounts for the grouping structure and within‑group correlation among lags. A new sparsification algorithm on the posterior motivated by Bayesian decision theory derives inclusion probabilities over lag groups, thus making the results easy to communicate without imposing sparsity a priori. An empirical application on nowcasting UK GDP growth suggests that group‑shrinkage in combination with the time‑varying components substantially increases nowcasting performance by reading signals from an economically meaningful subset of indicators, whereas the time‑varying components help by allowing the model to switch between indicators. Over the data release cycle, signals initially stem from survey data and then shift towards few ‘hard’ real activity indicators. During the Covid pandemic, the model performs relatively well since it shifts towards indicators for the service and housing sectors that capture the disruptions from economic lockdowns.
    Keywords: Bayesian MIDAS regressions; forecasting; time‑variation and fat tails; grouped horseshoe prior; decision analysis
    JEL: C11 C32 C44 C53 E37
    Date: 2023–06–02

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