{"id":124698,"date":"2024-01-23T09:15:43","date_gmt":"2024-01-23T08:15:43","guid":{"rendered":"https:\/\/www.wifo.ac.at\/publication\/124698\/"},"modified":"2025-10-22T22:09:05","modified_gmt":"2025-10-22T20:09:05","slug":"combining-country-specific-forecasts-when-forecasting-euro-area-macroeconomic-aggregates","status":"publish","type":"publication","link":"https:\/\/www.wifo.ac.at\/en\/publication\/124698\/","title":{"rendered":"Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates"},"content":{"rendered":"","protected":false},"featured_media":0,"template":"","class_list":["post-124698","publication","type-publication","status-publish","hentry"],"acf":{"subtitle":"","text":"European Monetary Union member countries' forecasts are often combined to obtain the forecasts of the Euro area macroeconomic aggregate variables. The aggregation weights which are used to produce the aggregates are often considered as combination weights. This paper investigates whether using different combination weights instead of the usual aggregation weights can help to provide more accurate forecasts. In this context, we examine the performance of equal weights, the least squares estimators of the weights, the combination method recently proposed by Hyndman et al. (Comput Stat Data Anal 55(9):2579-2589, 2011) and the weights suggested by shrinkage methods. We find that some variables like real GDP and the GDP deflator can be forecasted more precisely by using flexible combination weights. Furthermore, combining only forecasts of the three largest European countries helps to improve the forecasting performance. The persistence of the individual series seems to play an important role for the relative performance of the combination.","onlinedate":"2016-05-01 02:00:00","lang":"English","publication_series":"","publication_date":"20160501","publication_date_year":"","publication_date_full":false,"publication_num_pages":"","keywords":"[\"Forecast combination Aggregation Macroeconomic forecasting Hierarchical time series Persistence in data\"]","jelcodes":"[]","related_publications":"[]","pdf":null,"zip":null,"link":"http:\/\/www.springer.com\/10663","monthly_report":false,"monthly_report_main":false,"monthly_report_volume":"43","monthly_report_pages":"415-444","monthly_report_pages_sort":"415","issue":"2","journal":"Empirica","publisher":"","invisible":false,"external":true,"embargo_date":null,"types":[83083],"research_groups":[3500],"collaboration":"","persons":[15785],"persons_data":"[{\"personId\":15785,\"personName\":\"Jing Zeng\",\"organisationNames\":[]}]","clients":[],"subclients":[],"partners":[],"topics":[],"host_publication_title":"","host_publication_subtitle":"","place_of_publication":"","host_publication_editors":"[]","type_description":"","output_media":"","chapter":"","article_number":"","scientific_assistance":"[]","scientific_review":"[]","version":"","release_date":null,"expiration_date":null,"surveyor":"","research_assistance":"","edv":"","additional_info_de":"","additional_info_en":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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