利用随机森林预测美联储货币政策决策的方向

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-23 DOI:10.1002/for.3144
Jungyeon Yoon, Juanjuan Fan
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引用次数: 0

摘要

联邦基金目标利率通常被认为是反映美国经济状况的重要指标,个人投资者、金融公司和其他经济主体都非常关注。本文重点研究了 1994 年 1 月至 2022 年 6 月期间联邦基金目标利率的离散变化,并应用了序数森林模型(一种基于序数响应变量的随机森林预测方法)。我们用 45 个预测变量(包括宏观经济和金融变量以及前瞻性调查措施)检验了该模型的性能。为了准确、真实地衡量模型的性能,我们采用了单期提前样本外预测精度,而不是评估样本内拟合度。我们的实证结果表明,在以往关于联邦基金目标利率的研究中,序数森林法明显优于使用最新数据的基准方法。我们发现,从预测角度来看,TB 利差与 GDP、初请失业金人数和调查指标一样,信息量最大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Forecasting the direction of the Fed's monetary policy decisions using random forest

The federal funds target rate is commonly considered to be an important indicator of the state of the US economy and is of keen interest to individual investors, financial firms, and other economic agents. In this paper, we focus on the discrete changes in the federal funds target rate during the period from January 1994 to June 2022 and apply the ordinal forest model, a random forest-based prediction method for ordinal response variable. We examine the model's performance with 45 predictor variables which include macroeconomic and financial variables as well as forward-looking survey measures. For an accurate and honest measure of the model performance, we employ single-period-ahead out-of-sample forecasting accuracy instead of evaluating the in-sample fit. Our empirical results show the ordinal forest method significantly outperforms a benchmark that uses the most recent data among previous studies on federal funds target rate. We find that TB spread is the most informative from a forecasting perspective along with GDP, initial jobless claims, and survey measures.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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