{"title":"From macro to micro: Enhancing real GDP predictions through business tendency and bank loans surveys","authors":"Oguzhan Cepni , Furkan Emirmahmutoglu","doi":"10.1016/j.bir.2025.03.010","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines how effectively common factors, extracted using both the partial least squares method and principal component analysis from the business tendency survey and the banking loan tendency survey, can predict Turkiye’s economic growth. The findings indicate that integrating this survey data with macroeconomic variables has the potential to improve the accuracy of Turkiye’s real GDP growth predictions. When examined at the sector level, models employing factors from the Durable Consumer Goods sector exhibited the strongest predictive capabilities. Regarding firm size, models based on factors from large companies yielded superior out-of-sample prediction performance. Moreover, refining the prediction models by strategically reducing the number of factors using variable selection algorithms and choosing the most significant ones further enhanced their forecast accuracy. In conclusion, this study offers invaluable insights for policymakers, investors, and households in Turkiye by introducing a new approach to improving the accuracy of economic growth forecasts.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 4","pages":"Pages 770-780"},"PeriodicalIF":7.1000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Borsa Istanbul Review","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214845025000559","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study examines how effectively common factors, extracted using both the partial least squares method and principal component analysis from the business tendency survey and the banking loan tendency survey, can predict Turkiye’s economic growth. The findings indicate that integrating this survey data with macroeconomic variables has the potential to improve the accuracy of Turkiye’s real GDP growth predictions. When examined at the sector level, models employing factors from the Durable Consumer Goods sector exhibited the strongest predictive capabilities. Regarding firm size, models based on factors from large companies yielded superior out-of-sample prediction performance. Moreover, refining the prediction models by strategically reducing the number of factors using variable selection algorithms and choosing the most significant ones further enhanced their forecast accuracy. In conclusion, this study offers invaluable insights for policymakers, investors, and households in Turkiye by introducing a new approach to improving the accuracy of economic growth forecasts.
期刊介绍:
Peer Review under the responsibility of Borsa İstanbul Anonim Sirketi. Borsa İstanbul Review provides a scholarly platform for empirical financial studies including but not limited to financial markets and institutions, financial economics, investor behavior, financial centers and market structures, corporate finance, recent economic and financial trends. Micro and macro data applications and comparative studies are welcome. Country coverage includes advanced, emerging and developing economies. In particular, we would like to publish empirical papers with significant policy implications and encourage submissions in the following areas: Research Topics: • Investments and Portfolio Management • Behavioral Finance • Financial Markets and Institutions • Market Microstructure • Islamic Finance • Financial Risk Management • Valuation • Capital Markets Governance • Financial Regulations