Pub Date : 2016-08-11DOI: 10.1007/s41549-016-0004-3
S. Lima, M. Malgarini
{"title":"Does a Survey Based Capacity Utilization Measure Help Predicting Brazilian Output Gap in Real-Time?","authors":"S. Lima, M. Malgarini","doi":"10.1007/s41549-016-0004-3","DOIUrl":"https://doi.org/10.1007/s41549-016-0004-3","url":null,"abstract":"","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"1 1","pages":"119 - 139"},"PeriodicalIF":0.0,"publicationDate":"2016-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76874305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper establishes a reference chronology for the Russian economic cycle from the early 1980s to mid-2015. To detect peaks and troughs, we tested nine monthly indices as a reference series, three methods of seasonal adjustments (X-12-ARIMA, TRAMO/SEATS, and CAMPLET), and three methods for dating cyclical turning points (local min/max, Bry–Boschan method, and Markov-switching model). As these more or less formal methods led to different estimates, any sensible choice was only possible on the grounds of informal considerations. The final set of turning points looks plausible and separates expansions and contractions in an explicable manner, but further discussions are needed to establish a consensus between experts.
{"title":"Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices","authors":"S. Smirnov, N. Kondrashov, Anna V. Petronevich","doi":"10.2139/ssrn.2720134","DOIUrl":"https://doi.org/10.2139/ssrn.2720134","url":null,"abstract":"Abstract\u0000This paper establishes a reference chronology for the Russian economic cycle from the early 1980s to mid-2015. To detect peaks and troughs, we tested nine monthly indices as a reference series, three methods of seasonal adjustments (X-12-ARIMA, TRAMO/SEATS, and CAMPLET), and three methods for dating cyclical turning points (local min/max, Bry–Boschan method, and Markov-switching model). As these more or less formal methods led to different estimates, any sensible choice was only possible on the grounds of informal considerations. The final set of turning points looks plausible and separates expansions and contractions in an explicable manner, but further discussions are needed to establish a consensus between experts.","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"16 1","pages":"53-73"},"PeriodicalIF":0.0,"publicationDate":"2016-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77150073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-01-01DOI: 10.1007/s41549-016-0010-5
K. Lahiri, Yongchen Zhao
{"title":"Determinants of Consumer Sentiment Over Business Cycles: Evidence from the US Surveys of Consumers","authors":"K. Lahiri, Yongchen Zhao","doi":"10.1007/s41549-016-0010-5","DOIUrl":"https://doi.org/10.1007/s41549-016-0010-5","url":null,"abstract":"","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"28 1","pages":"187-215"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41549-016-0010-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72503382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Business surveys indicators represent an important tool in economic analysis and forecasting practices. While there is wide consensus on the coincident properties of such data, there is mixed evidence on their ability to forecast macroeconomic developments in the short term. In this study we extend the previous research on business surveys predictive content by examining for the first time the leading properties of the main business survey indicators coming from the Italian survey on inflation and growth expectations (SIGE). To this end we provide a complete characterization of the business cycle leading/coincident properties of SIGE data (turning points, average duration, synchronization etc.) with respect to the National Accounts reference series using both non parametric approaches (i.e. Harding and Pagan in J Monet Econ 49(2):365–381, 2002) and econometric models (discrete and continuous dynamic single equation models). Overall the results indicate that in both the approaches SIGE business indicators are able to early detect turning points of their corresponding national account reference series in almost all cases. Overall, the average lead of troughs is found to be higher than the average lead of peaks.
商业调查指标是经济分析和预测实践中的重要工具。虽然人们对这些数据的一致性有广泛的共识，但关于它们预测短期宏观经济发展的能力，证据不一。在本研究中，我们通过首次检查来自意大利通胀和增长预期调查(SIGE)的主要商业调查指标的领先属性，扩展了之前对商业调查预测内容的研究。为此，我们使用非参数方法(即J Monet Econ 49(2): 365-381, 2002)和计量经济学模型(离散和连续动态单方程模型)提供了关于国民账户参考系列的SIGE数据的商业周期领先/同步属性(转折点，平均持续时间，同步等)的完整特征。总体而言，结果表明，在这两种方法中，SIGE业务指标几乎在所有情况下都能够早期发现其相应的国民账户参考系列的转折点。总体而言，发现波谷的平均导程高于波峰的平均导程。
{"title":"The Predictive Content of Business Survey Indicators: Evidence from SIGE","authors":"Tatiana Cesaroni, S. Iezzi","doi":"10.2139/ssrn.2722518","DOIUrl":"https://doi.org/10.2139/ssrn.2722518","url":null,"abstract":"Business surveys indicators represent an important tool in economic analysis and forecasting practices. While there is wide consensus on the coincident properties of such data, there is mixed evidence on their ability to forecast macroeconomic developments in the short term. In this study we extend the previous research on business surveys predictive content by examining for the first time the leading properties of the main business survey indicators coming from the Italian survey on inflation and growth expectations (SIGE). To this end we provide a complete characterization of the business cycle leading/coincident properties of SIGE data (turning points, average duration, synchronization etc.) with respect to the National Accounts reference series using both non parametric approaches (i.e. Harding and Pagan in J Monet Econ 49(2):365–381, 2002) and econometric models (discrete and continuous dynamic single equation models). Overall the results indicate that in both the approaches SIGE business indicators are able to early detect turning points of their corresponding national account reference series in almost all cases. Overall, the average lead of troughs is found to be higher than the average lead of peaks.","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"47 1","pages":"75-104"},"PeriodicalIF":0.0,"publicationDate":"2015-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76070821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we evaluate the forecasting performance of employment expectations for employment growth in 15 European states. Our data cover the period from the first quarter 1998 to the fourth quarter 2014. With in-sample analyses and pseudo out-of-sample exercises, we find that for most of the European states considered, the survey-based indicator model outperforms common benchmark models. It is therefore a powerful tool for generating more accurate employment forecasts. We observe the best results for one quarter ahead predictions that are primarily the aim of the survey question. However, employment expectations also work well for longer forecast horizons in some countries.
{"title":"Forecasting Employment in Europe: Are Survey Results Helpful?","authors":"R. Lehmann, A. Weyh","doi":"10.2139/ssrn.2337564","DOIUrl":"https://doi.org/10.2139/ssrn.2337564","url":null,"abstract":"In this paper we evaluate the forecasting performance of employment expectations for employment growth in 15 European states. Our data cover the period from the first quarter 1998 to the fourth quarter 2014. With in-sample analyses and pseudo out-of-sample exercises, we find that for most of the European states considered, the survey-based indicator model outperforms common benchmark models. It is therefore a powerful tool for generating more accurate employment forecasts. We observe the best results for one quarter ahead predictions that are primarily the aim of the survey question. However, employment expectations also work well for longer forecast horizons in some countries.","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"44 1","pages":"81-117"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83514830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1007/s41549-021-00058-2
M. Soloschenko, Enzo Weber
{"title":"Trend-Cycle Interactions and the Subprime Crisis: Analysis of US and Canadian Output","authors":"M. Soloschenko, Enzo Weber","doi":"10.1007/s41549-021-00058-2","DOIUrl":"https://doi.org/10.1007/s41549-021-00058-2","url":null,"abstract":"","PeriodicalId":55850,"journal":{"name":"Journal of Business Cycle Research","volume":"6 1","pages":"109 - 128"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82526751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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