[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127259-en":3,"doc-seo-127259-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127259,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach","Accurate macroeconomic forecasts are essential for effective policy decisions, yet forecast precision depends on the accuracy of underlying assumptions. This paper studies how assumption errors translate into forecast accuracy, introducing the average squared assumption error (ASAE) as an instrument to address endogeneity. The analysis applies double/debiased machine learning (DML) and partial linear instrumental variable (PLIV) models to Germany’s GDP growth forecasts. Findings show OLS underestimates assumption-error effects (notably for world trade), while DML mitigates endogeneity, reduces multicollinearity, and captures nonlinearities; the oil-price assumption-error impact on GDP forecast errors remains ambiguous.","Heinisch, Katja; Scaramella, Fabio; Schult, Christoph  \nWorking Paper  \nAssumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach  \nIWH Discussion Papers, No. 6/2025  \nProvided in Cooperation with:  \nHalle Institute for Economic Research (IWH)– Member of the Leibniz Association  \nSuggested Citation: Heinisch, Katja; Scaramella, Fabio; Schult, Christoph (2025) : Assumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach, IWH Discussion Papers, No. 6/2025, Halle Institute for Economic Research (IWH), Halle (Saale), [https://doi.org/10.18717/dprpy3-ff77](https://doi.org/10.18717/dprpy3-ff77)  \nThis Version is available at:  \n[https://hdl.handle.net/10419/318189](https://hdl.handle.net/10419/318189)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \nDiscussion Papers  \nNo. 6  \nMay 2025  \n Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach  \nKatja Heinisch, Fabio Scaramella, Christoph Schult  \nII IWH Discussion Papers No . 6/2025  \n[https://doi.org/10.18717/dprpy3-ff77](https://doi.org/10.18717/dprpy3-ff77)  \nAuthors  \nKatja Heinisch  \nCorresponding author  \nHalle Institute for Economic Research (IWH)– Member of the Leibniz Association, Department of Macroeconomics [E-mail: katja.heinisch@iwh-halle.de](E-mail: katja.heinisch@iwh-halle.de)[ ](E-mail: katja.heinisch@iwh-halle.de)Tel +49 345 7753 836  \nFabio Scaramella  \nUniversity of Bologna  \nChristoph Schult  \nHalle Institute for Economic Research (IWH)– Member of the Leibniz Association, Department of Macroeconomics  \nE-mail: [christoph.schult@iwh-halle.de](christoph.schult@iwh-halle.de)[ ](christoph.schult@iwh-halle.de)Tel +49 345 7753 806  \nThe responsibility for discussion papers lies solely with the individual authors. The views expressed herein do not necessarily represent those of IWH. The papers represent preliminary work and are circulated to encourage discussion with the authors. Citation of the discussion papers should account for their provisional character; a revised version maybe available directly from the authors.  \nComments and suggestions on the methods and results presented are welcome.  \nIWH Discussion Papers are indexed in RePEc-EconPapers and in ECONIS.  \nEditor  \nHalle Institute for Economic Research (IWH)– Member of the Leibniz Association  \nAddress: Kleine Maerkerstrasse 8  \nD-06108 Halle (Saale), Germany Postal Address: P. O. Box 11 03 61 D-06017 Halle (Saale), Germany  \nTel +49 345 7753 60 Fax +49 345 7753 820  \n[www.iwh-halle.de](www.iwh-halle.de)  \n[ISSN 2194-2188](ISSN 2194-2188)  \nAssumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach*  \nAbstract  \nAccurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptio","cbCailNv0wK2gGno","https://ap.wps.com/l/cbCailNv0wK2gGno","pdf",4217451,1,39,"English","en",105,"# Motivation\n## Forecast accuracy and the role of assumptions\n# Methodology and instruments\n## ASAE as an instrument for endogeneity\n## DML and PLIV modeling\n# Empirical analysis\n## GDP growth forecasts for Germany\n## Effects of oil price, exchange rate, and world trade","[{\"question\":\"What is the main goal of the paper on macroeconomic forecast accuracy?\",\"answer\":\"To quantify how assumption errors affect forecast accuracy, linking the reliability of forecasts to the correctness of underlying assumptions.\"},{\"question\":\"How does the paper address endogeneity in estimating assumption-error effects?\",\"answer\":\"It introduces the average squared assumption error (ASAE) as a valid instrument and uses double/debiased machine learning (DML) alongside partial linear instrumental variable (PLIV) models.\"},{\"question\":\"What do the results say about OLS versus DML for assumption errors?\",\"answer\":\"Traditional OLS systematically underestimates the influence of assumption errors, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities.\"}]","Assumption Errors and Forecast Accuracy: A Partial Linear Instrumental Variable and Double Machine Learning Approach | 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