{"title":"利用随机森林预测美联储货币政策决策的方向","authors":"Jungyeon Yoon, Juanjuan Fan","doi":"10.1002/for.3144","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2848-2859"},"PeriodicalIF":3.4000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3144","citationCount":"0","resultStr":"{\"title\":\"Forecasting the direction of the Fed's monetary policy decisions using random forest\",\"authors\":\"Jungyeon Yoon, Juanjuan Fan\",\"doi\":\"10.1002/for.3144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":47835,\"journal\":{\"name\":\"Journal of Forecasting\",\"volume\":\"43 7\",\"pages\":\"2848-2859\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3144\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forecasting\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/for.3144\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forecasting","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/for.3144","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
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.
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.