使用机器学习方法和多个大型数据集进行预测[公式省略]

IF 2 Q2 ECONOMICS
Nikoleta Anesti, Eleni Kalamara, George Kapetanios
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引用次数: 0

摘要

本文探讨了机器学习技术在利用多个大型数据集预测宏观经济变量方面的实用性。将调查的预测内容与来自报纸文章和标准宏观经济数据集的基于文本的指标进行了比较,从而扩展了每个数据集在预测经济活动方面的贡献。在线性模型中,岭回归和偏最小二乘法模型在大多数预测期限内的收益最大,而在非线性机器学习模型中,支持向量回归在较短期限内的表现要好于神经网络和随机森林,前者可在两年内做出更准确的预测。基于文本的指标与调查具有相似的信息内容,尽管将这两个数据集结合起来,在大多数预测范围内都能提供更准确的预测。对于大多数模型和数据集来说,最大的预测收益绝大多数集中在较短的时间跨度上,并且在一年后会显著下降。非线性机器学习模型在大金融危机期间似乎最有用,而在较正常时期的表现与线性模型类似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting with Machine Learning methods and multiple large datasets[formula omitted]
The usefulness of machine learning techniques for forecasting macroeconomic variables using multiple large datasets is considered. The predictive content of surveys is compared with text-based indicators from newspaper articles and a standard macroeconomic dataset, extending the evidence on the contribution of each dataset in predicting economic activity. Among the linear models, the Ridge regression and the Partial Least Squares models report the largest gains consistently for most of the forecasting horizons, and among the non linear machine learning models, Support Vector Regression performs better at shorter horizons compared to the Neural Networks and Random Forest that yield more accurate forecasts up to two years ahead. Text based indicators have similar informational content to surveys, albeit combining the two datasets provides with more accurate forecasts for most of the forecast horizons. The largest forecasting gains are overwhelmingly concentrated at the shorter horizons for the majority of models and datasets and they decrease significantly after one year. Non-linear machine learning models appear to be mostly useful during the Great Financial Crisis and perform similarly to their linear counterparts in more normal periods.
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来源期刊
CiteScore
3.10
自引率
10.50%
发文量
84
期刊介绍: Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. It publishes research papers in all aspects of econometrics and statistics and comprises of the two sections Part A: Econometrics and Part B: Statistics.
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