{"title":"GDP临近预测的机器学习方法:新兴市场经验","authors":"Saurabh Ghosh, Abhishek Ranjan","doi":"10.59091/1410-8046.2055","DOIUrl":null,"url":null,"abstract":"The growth rate of real Gross Domestic Product (GDP), as measured by the National Statistical Office of India, is an important metric for monetary policy making. Because GDP is released with a significant lag, particularly for the emerging market economies, this article presents various methodologies for nowcasting and forecasting GDP, using both traditional time series and machine learning methods. Further, considering the importance of forward-looking information, our nowcasting model incorporates financial market data and an economic uncertainty index, in addition to high-frequency traditional macroeconomic indicators. Our findings suggest an improvement in the performance of nowcasting using a hybrid of machine learning and conventional time series methods.","PeriodicalId":36737,"journal":{"name":"Buletin Ekonomi Moneter dan Perbankan","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A MACHINE LEARNING APPROACH TO GDP NOWCASTING: AN EMERGING MARKET EXPERIENCE\",\"authors\":\"Saurabh Ghosh, Abhishek Ranjan\",\"doi\":\"10.59091/1410-8046.2055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The growth rate of real Gross Domestic Product (GDP), as measured by the National Statistical Office of India, is an important metric for monetary policy making. Because GDP is released with a significant lag, particularly for the emerging market economies, this article presents various methodologies for nowcasting and forecasting GDP, using both traditional time series and machine learning methods. Further, considering the importance of forward-looking information, our nowcasting model incorporates financial market data and an economic uncertainty index, in addition to high-frequency traditional macroeconomic indicators. Our findings suggest an improvement in the performance of nowcasting using a hybrid of machine learning and conventional time series methods.\",\"PeriodicalId\":36737,\"journal\":{\"name\":\"Buletin Ekonomi Moneter dan Perbankan\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Buletin Ekonomi Moneter dan Perbankan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59091/1410-8046.2055\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Buletin Ekonomi Moneter dan Perbankan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59091/1410-8046.2055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 1
摘要
印度国家统计局(National Statistical Office of India)测量的实际国内生产总值(GDP)增长率是制定货币政策的重要指标。由于GDP的发布具有明显的滞后性,特别是对于新兴市场经济体,本文介绍了使用传统时间序列和机器学习方法进行临近预测和预测GDP的各种方法。此外,考虑到前瞻性信息的重要性,我们的临近预测模型除了高频传统宏观经济指标外,还纳入了金融市场数据和经济不确定性指数。我们的研究结果表明,使用机器学习和传统时间序列方法的混合方法可以改善临近预报的性能。
A MACHINE LEARNING APPROACH TO GDP NOWCASTING: AN EMERGING MARKET EXPERIENCE
The growth rate of real Gross Domestic Product (GDP), as measured by the National Statistical Office of India, is an important metric for monetary policy making. Because GDP is released with a significant lag, particularly for the emerging market economies, this article presents various methodologies for nowcasting and forecasting GDP, using both traditional time series and machine learning methods. Further, considering the importance of forward-looking information, our nowcasting model incorporates financial market data and an economic uncertainty index, in addition to high-frequency traditional macroeconomic indicators. Our findings suggest an improvement in the performance of nowcasting using a hybrid of machine learning and conventional time series methods.