[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120011-en":3,"doc-seo-120011-105":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},120011,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Forecasting Inflation in Norway Using Machine Learning - Master’s Thesis 2024","This master’s thesis evaluates the effectiveness of machine learning models, including Random Forest and LSTM, for forecasting post-Covid inflation trends in Norway. Results show that LSTM outperforms conventional benchmark approaches and an ARIMA model within a 12-month forecast window, targeting the sharp inflation increase after the pandemic. The evidence is limited to the postCovid Norwegian economic environment, with no testing across other conditions. The work informs the potential of ML in macroeconomic forecasting and outlines directions for future research to address current limitations.","Master’s Thesis 2024 30 ECTS  \nSchool of Economics and Business  \nForecasting Inflation in Norway Using Machine Learning  \nMartin Bergsholm Nesse Master of Science in Data Science  \nAcknowledgements  \nI would like to express my deepest gratitude to a number of individuals whose guidance and support were instrumental in the completion of this thesis.  \nFirstly, my sincere thanks to Petter Nesse for his meticulous proofreading and invaluable feedback throughout the process.  \nI am also grateful to my co-supervisor, Kristian Hovde Liland, for his insightful feedback on the methodology and programming aspects of this thesis.  \nA special acknowledgment goes to Norges Bank for providing the necessary data and for their insightful comments regarding the explanatory variables.  \nI must also thank professor Øystein Thøgersen at the Norwegian School of Economics (NHH), my main supervisor for my previous master’s thesis as cited in Nesse and Haug (2016), for his excellent guidance and for encouraging me to pursue further research. It was during the work under his supervision that I first considered the application of machine learning in economic forecasting.  \nAdditionally, I extend my gratitude to the National Library for providing an excellent solution for performing sentiment analysis on a wide range of newspapers.  \nMost importantly, I would like to express my deepest appreciation to my main supervisor, Associate Professor Roberto J. Garcia, for his extensive feedback and exceptional guidance throughout the writing process.  \nNorwegian University of Life Sciences  \n˚As, May, 2024  \nAbstract  \nThis thesis investigates the efficacy of machine learning (ML) models, such as Random Forest and Long-short-term memory (LSTM), in forecasting post-Covid inflation trends in Norway. The research demonstrates that LSTM models outperform traditional benchmark models and an autoregressive integrated moving average (ARIMA) model within a 12-month forecast horizon, focusing on the sudden surge in inflation following the pandemic.  \nThe findings are constrained to the specific economic conditions of the postCovid period in Norway, with no testing performed under other economic circumstances. This thesis contributes to the understanding of ML’s potential in economic forecasting and suggests pathways for future research to overcome its limitations and explore new methodologies in the field of economic analysis.  \nUse of AI  \nAI has been utilized to enhance various aspects of this thesis. It has aided in exploring relevant theories and literature, enhancing the Python coding process, results analysis, and correcting spelling and grammatical errors, and improving the overall flow of the text. AI-services include Grammarly and ChatGPT. All suggestions from AI have been treated as exactly that—suggestions.  \nContents  \n1 Introduction 10  \n1.1 Motivation ................................ 10  \n1.2 Objective ................................ 13  \n1.3 Organisation of the Thesis ....................... 13  \n2 Background 14  \n2.1 Macroeconomic indicators for Norway ................. 14  \n2.2 The Norwegian central bank ...................... 19  \n2.3 Inflation and monetary policy in Norway from 1990 to 2022 ..... 20  \n3 Theory and related literature 25  \n3.1 Inflation ................................. 25  \n3.1.1 Quantitative theory of money ................. 25  \n3.1.2 Monetary theory of inflation .................. 26  \n3.1.3 Demand pull theory ...................... 26  \n3.1.4 Cost push theory ........................ 27  \n3.1.5 Rational expectations revolution ................ 28  \n3.1.6 New neoclassical synthesis (NNS) ............... 28  \n3.2 Expected inflation ............................ 30  \n3.2.1 Phillips curves .......................... 30  \n3.2.2 Natural rate of interest ..................... 32  \n3.3 Output gap ............................... 34  \n3.3.1 The macro supply relation ................... 34  \n3.3.2 The Phillips-relation ......................","cbCaiow7QtRhSX7J","https://ap.wps.com/l/cbCaiow7QtRhSX7J","pdf",6878493,1,272,"English","en",105,"# Introduction\n## Motivation\n## Objective\n## Organisation of the Thesis\n# Background\n## Macroeconomic indicators for Norway\n## The Norwegian central bank\n## Inflation and monetary policy in Norway from 1990 to 2022\n# Theory and related literature\n## Inflation\n## Expected inflation\n## Output gap\n## Statistical tests\n## Literature reviews\n# Method and data\n## Data variables\n## Models","[{\"question\":\"Which machine learning models are used for forecasting inflation in Norway?\",\"answer\":\"The thesis uses machine learning models such as Random Forest and LSTM, alongside benchmark models and an ARIMA model for comparison.\"},{\"question\":\"How does LSTM perform compared with traditional benchmark models and ARIMA?\",\"answer\":\"Within a 12-month forecast horizon, LSTM outperforms traditional benchmark approaches and the ARIMA model for post-Covid inflation forecasting.\"},{\"question\":\"What limitations apply to the study’s findings?\",\"answer\":\"Findings are constrained to the specific postCovid economic conditions in Norway, and no evaluation is performed under other economic circumstances.\"}]","Forecasting Inflation in Norway Using Machine Learning - Master’s Thesis 2024 | PDF",1785727701,685,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-inflation-in-norway-using-machine-learning-masters-thesis-2024","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-inflation-in-norway-using-machine-learning-masters-thesis-2024/120011/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used for forecasting inflation in Norway?","Question",{"text":75,"@type":76},"The thesis uses machine learning models such as Random Forest and LSTM, alongside benchmark models and an ARIMA model for comparison.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does LSTM perform compared with traditional benchmark models and ARIMA?",{"text":80,"@type":76},"Within a 12-month forecast horizon, LSTM outperforms traditional benchmark approaches and the ARIMA model for post-Covid inflation forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"What limitations apply to the study’s findings?",{"text":84,"@type":76},"Findings are constrained to the specific postCovid economic conditions in Norway, and no evaluation is performed under other economic circumstances.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]