{"title":"Forecasting with Leading Indicators by means of the Principal Covariate Index","authors":"C. Heij, D. Dijk, P. Groenen","doi":"10.1787/JBCMA-2011-5KGDWLPZS79V","DOIUrl":"https://doi.org/10.1787/JBCMA-2011-5KGDWLPZS79V","url":null,"abstract":"A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board’s Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. JEL Classification: C32, C53, E27 Keywords: index construction, business cycles, principal component, principal covariate, time series forecasting, variable selection","PeriodicalId":313514,"journal":{"name":"Oecd Journal: Journal of Business Cycle Measurement and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130743048","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}
{"title":"Trying to assess the quality of macroeconomic data","authors":"Jochen Hartwig","doi":"10.1787/JBCMA-V2008-ART3-EN","DOIUrl":"https://doi.org/10.1787/JBCMA-V2008-ART3-EN","url":null,"abstract":"Macroeconomic data are indispensable for modern governance, yet it is often unclear how reliable these data are. The production process of macroeconomic data inside the statistical offices is often not very transparent for the general public. Bystanders usually have no choice but to take for granted the published data because criteria by which to judge data quality are wanting. Hoping to contribute to a better understanding of the quality of macroeconomic data, this paper proposes several plausibility checks and applies them to recently published Swiss labour productivity growth figures. Although the proposed checks cannot \"prove\" or \"disprove\" the official data, they are capable of either strengthening our confidence in the official data or, alternatively, of casting them into doubt. Policy debates drawing on official data will hardly be able to ignore differences in the degree of confidence with which these data are held to be accurate.","PeriodicalId":313514,"journal":{"name":"Oecd Journal: Journal of Business Cycle Measurement and Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124588390","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}