nep-for New Economics Papers
on Forecasting
Issue of 2024‒03‒04
three 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. What drives the European carbon market? Macroeconomic factors and forecasts By Andrea Bastianin; Elisabetta Mirto; Yan Qin; Luca Rossini
  3. Nowcasting Madagascar's real GDP using machine learning algorithms By Franck Ramaharo; Gerzhino Rasolofomanana

  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 category. 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: 2024–02
  2. By: Andrea Bastianin (University of Milan and Fondazione Eni Enrico Mattei); Elisabetta Mirto (University of Milan); Yan Qin (London Stock Exchange Group); Luca Rossini (University of Milan and Fondazione Eni Enrico Mattei)
    Abstract: Putting a price on carbon – with taxes or developing carbon markets – is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the EU Emissions Trading Scheme (EU ETS). We aim to uncover supply- and demand-side forces that can contribute to improving the prediction accuracy of models at short- and medium-term horizons. We show that a simple Bayesian Vector Autoregressive (BVAR) model, augmented with either one or two factors capturing a set of predictors affecting the price of carbon, provides substantial accuracy gains over a wide set of benchmark forecasts, including survey expectations and forecasts made available by data providers. We extend the study to verified emissions and demonstrate that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market. Our results are relevant for policymakers and market practitioners interested in quantifying the desired and unintended macroeconomic effects of monitoring the carbon market dynamics.
    Keywords: Bayesian inference, Carbon prices, Climate Changes, EU ETS, Forecasting
    JEL: C11 C32 C53 Q02 Q50
    Date: 2024–02
  3. By: Franck Ramaharo; Gerzhino Rasolofomanana
    Abstract: We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1--2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.
    Date: 2023–12

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