Dongkoo Kim, Tae-hwan Rhee, K. Ryu, Changmock Shin
{"title":"Crowdsourcing of Economic Forecast – Combination of Forecasts using Bayesian Model Averaging","authors":"Dongkoo Kim, Tae-hwan Rhee, K. Ryu, Changmock Shin","doi":"10.2139/ssrn.2618394","DOIUrl":null,"url":null,"abstract":"Economic forecasts are quite essential in our daily lives, which is why many research institutions periodically make and publish forecasts of main economic indicators. We ask (1) whether we can consistently have a better prediction when we combine multiple forecasts of the same variable and (2) if we can, what will be the optimal method of combination. We linearly combine multiple linear combinations of existing forecasts to form a new forecast ('combination of combinations'), and the weights are given by Bayesian model averaging. In the case of forecasts on Germany's real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2618394","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Economic forecasts are quite essential in our daily lives, which is why many research institutions periodically make and publish forecasts of main economic indicators. We ask (1) whether we can consistently have a better prediction when we combine multiple forecasts of the same variable and (2) if we can, what will be the optimal method of combination. We linearly combine multiple linear combinations of existing forecasts to form a new forecast ('combination of combinations'), and the weights are given by Bayesian model averaging. In the case of forecasts on Germany's real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors.