nep-for New Economics Papers
on Forecasting
Issue of 2021‒09‒13
five papers chosen by
Rob J Hyndman
Monash University

  1. Forecast Pooling or Information Pooling During Crises? MIDAS Forecasting of GDP in a Small Open Economy By Chow, Hwee Kwan; Han, Daniel
  2. Forecasting Dynamic Term Structure Models with Autoencoders By Castro-Iragorri, C; Ramírez, J
  3. Forecasting Regional Lean Hog Basis using GARCH Models By Dinges, Kaitlyn M.; Schroeder, Ted C.
  4. Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs By Lucien Boulet
  5. Forecasting Net Farm Income Based on Historical Farm Data and Future Prices By Ibendahl, Gregory A.

  1. By: Chow, Hwee Kwan (School of Economics, Singapore Management University); Han, Daniel (School of Economics, Singapore Management University)
    Abstract: This study compares two distinct approaches, pooling forecasts from single indicator MIDAS models versus pooling information from indicators into factor MIDAS models, for short-term Singapore GDP growth forecasting with a large ragged-edge mixed frequency dataset. We investigate their relative predictive performance in a pseudo-out-of-sample forecasting exercise from 2007Q4 to 2020Q3. In the stable growth non-crisis period, no substantial difference in predictive performance is found across forecast models. We find factor MIDAS models dominate both the quarterly benchmark model and the forecast pooling strategy by wide margins in the Global Financial Crisis and the Covid-19 crisis. Reflecting the small open nature of the economy, pooling single indicator forecasts from a small subgroup of foreign-related indicators beats the benchmark, offering a quick method to incorporate timely information for practitioners who have difficulty updating a large dataset. Nonetheless, the information pooling approach retains its superior ability at tracking rapid output changes during crises.
    Keywords: Forecast evaluation; Factor MIDAS; pooling GDP forecasts; global financial crisis; Covid-19 pandemic crisis
    JEL: C22 C53 C55
    Date: 2021–07–01
  2. By: Castro-Iragorri, C; Ramírez, J
    Abstract: Principal components analysis (PCA) is a statistical approach to build factor models in finance. PCA is also a particular case of a type of neural network known as an autoencoder. Recently, autoencoders have been successfully applied in financial applications using factor models, Gu et al. (2020), Heaton and Polson (2017). We study the relationship between autoencoders and dynamic term structure models; furthermore we propose different approaches for forecasting. We compare the forecasting accuracy of dynamic factor models based on autoencoders, classical models in term structure modelling proposed in Diebold and Li (2006) and neural network-based approaches for time series forecasting. Empirically, we test the forecasting performance of autoencoders using the U.S. yield curve data in the last 35 years. Preliminary results indicate that a hybrid approach using autoencoders and vector autoregressions framed as a dynamic term structure model provides an accurate forecast that is consistent throughout the sample. This hybrid approach overcomes in-sample overfitting and structural changes in the data.
    Keywords: autoencoders, factor models, principal components, recurrentneural networks
    JEL: C45 C53 C58
    Date: 2021–07–29
  3. By: Dinges, Kaitlyn M.; Schroeder, Ted C.
    Keywords: Marketing, Agribusiness, Risk and Uncertainty
    Date: 2021–08
  4. By: Lucien Boulet
    Abstract: Several academics have studied the ability of hybrid models mixing univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and neural networks to deliver better volatility predictions than purely econometric models. Despite presenting very promising results, the generalization of such models to the multivariate case has yet to be studied. Moreover, very few papers have examined the ability of neural networks to predict the covariance matrix of asset returns, and all use a rather small number of assets, thus not addressing what is known as the curse of dimensionality. The goal of this paper is to investigate the ability of hybrid models, mixing GARCH processes and neural networks, to forecast covariance matrices of asset returns. To do so, we propose a new model, based on multivariate GARCHs that decompose volatility and correlation predictions. The volatilities are here forecast using hybrid neural networks while correlations follow a traditional econometric process. After implementing the models in a minimum variance portfolio framework, our results are as follows. First, the addition of GARCH parameters as inputs is beneficial to the model proposed. Second, the use of one-hot-encoding to help the neural network differentiate between each stock improves the performance. Third, the new model proposed is very promising as it not only outperforms the equally weighted portfolio, but also by a significant margin its econometric counterpart that uses univariate GARCHs to predict the volatilities.
    Date: 2021–08
  5. By: Ibendahl, Gregory A.
    Keywords: Teaching/Communication/Extension/Profession, Agricultural and Food Policy, Agricultural Finance
    Date: 2021–08

This nep-for issue is ©2021 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.