In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. In this paper, we pursue another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) gradient descent with sparsification and (4) meta-heuristic algorithms. Comprehensive simulation studies are conducted to compare their variable selection accuracy and prediction performance under different scenarios. Among the reviewed methods, a meta-heuristic algorithm called sequential Monte Carlo algorithm performs the best. Surprisingly the classical forward selection is comparable to it and better than other more sophisticated algorithms. In addition, we apply these VS methods on economic forecasting and compare with the popular FA approach. It turns out for employment rate and CPI inflation, some VS methods can achieve considerable improvement over FA, and the selected predictors can be well explained by economic theories.
Macroeconomic Forecasting with Large Language Models
School of Economics and Finance, Queen Mary University of London（伦敦大学女王学院经济与金融学院）；School of Business and Economics, Brandeis University（布兰迪大学商学院与经济学学院）
Forecasting the Price of Carbon with Macroeconomic and Financial variables
University of Milan（米兰大学）；Fondazione Eni Enrico Mattei (FEEM)（埃尼·恩里科·马泰伊基金会）；Study Center Gerzensee, Foundation of the Swiss National Bank（瑞士国家银行格尔岑塞研究中心）；ClearBlue Markets
Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks
SAFIR, Sorbonne University Abu Dhabi（索邦大学阿布扎比分校 SAFIR）；BITS Pilani, Hyderabad Campus（印度皮拉尼博拉理工学院海得拉巴校区）；Fidelity Investments（富达投资）；Sorbonne Center for Artificial Intelligence, Sorbonne University（索邦大学人工智能中心）
Macroeconomic forecasting with LSTM and mixed frequency time series data
Faculty of Economics, Khon Kaen University（孔敬大学经济学院）；Khon Kaen University（孔敬大学）
本文展示了长短期记忆（LSTM）在应用于不同频率采样的宏观经济时间序列数据时的潜力。我们首先介绍了当对所有低频输出和高频变量对应用相同的失配比时，传统的LSTM模型如何适应在混合频率下观察到的时间序列。为了将LSTM推广到多个失配率的情况，我们采用了无限制混合数据采样（U-MIDAS）方案（Foroni et al.，2015年）引入LSTM架构。我们评估通过bothMonte Carlo模拟和实证应用的样本预测性能。我们提出的模型优于限制MIDAS模型，即使在一个设置有利于MIDAS估计。对于现实世界的应用，我们研究预测泰国实际GDP的季度增长率，使用大量的宏观经济指标季度和月度。我们使用U-MIDAS方案的LSTM在所有范围内都轻松击败了简单的基准AR（1）模型，但仅在一个月和六个月的时间内优于强基准单变量LSTM。尽管如此，我们发现，我们提出的模型可能是非常有帮助的，在大的经济低迷时期的短期预测。模拟和实验结果似乎支持我们提出的LSTM with U-MIDAS方案用于临近预报应用。
Olin Business School, Washington University in St. Louis（华盛顿大学圣路易斯分校奥林商学院）；Chaifetz School of Business, Saint Louis University（圣路易斯大学蔡菲兹商学院）；Center for Economic Behavior and Decision-Making, Zhejiang University of Finance and Economics（浙江财经大学经济行为与决策研究中心）；Department of Economics, University of Chicago（芝加哥大学经济学系）
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
School of Economics and Finance, Queen Mary University of London（伦敦大学玛丽女王学院经济与金融学院）；School of Business and Economics, Brandeis University（布兰迪大学商学院与经济学院）
Forecasting Foreign Exchange Rate: A Multivariate Comparative Analysis between Traditional Econometric, Contemporary Machine Learning & Deep Learning Techniques
Birla Institute of Technology and Science, Pilani（博拉理工与科学大学皮拉尼分校）
A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference
Department of Economics University of California, Riverside（经济系加州大学河滨分校）
