{"title":"关于经济周期预测","authors":"Huiwen Lai, Eric C. Y. Ng","doi":"10.1186/s11782-020-00085-3","DOIUrl":null,"url":null,"abstract":"We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.","PeriodicalId":54175,"journal":{"name":"Frontiers of Business Research in China","volume":"16 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"On business cycle forecasting\",\"authors\":\"Huiwen Lai, Eric C. Y. Ng\",\"doi\":\"10.1186/s11782-020-00085-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.\",\"PeriodicalId\":54175,\"journal\":{\"name\":\"Frontiers of Business Research in China\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2020-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Business Research in China\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1186/s11782-020-00085-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Business Research in China","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1186/s11782-020-00085-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.
期刊介绍:
Frontiers of Business Research in China (FBR) is a double-blind refereed quarterly journal in business research. FBR offers a multidisciplinary forum for academics, practitioners, and policy makers that focuses on business administration, and encourages interdisciplinary studies and interactions between Chinese and international researchers. FBR publishes original academic and practical research articles that extend, test, or build management theories, as well as contributions to business administration practice, either in the Greater China region or beyond. The Journal also publishes related commentaries and case studies. FBR invites submissions of high-quality manuscripts in all areas of business administration, without limitations on research methods. Major areas of interest include, but are not limited to: Accounting, Finance, Human resources, International business, Marketing, Management information systems, Operations management, Organizational behavior, and Strategic management.