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Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?

GDP is used to measure the economic state of a country and accurate forecasts of it is therefore important. Using the Economic Tendency Survey we investigate forecasting quarterly GDP growth using the data mining technique Random Forest. Comparisons are made with a benchmark AR(1) and an ad hoc linear model built on the most important variables suggested by the Random Forest. Evaluation by forecasting shows that the Random Forest makes the most accurate forecast supporting the theory that there are benefits to using Random Forests on economic time series.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-243028
Date January 2015
CreatorsAdriansson, Nils, Mattsson, Ingrid
PublisherUppsala universitet, Statistiska institutionen, Uppsala universitet, Statistiska institutionen
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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