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Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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. 

Place, publisher, year, edition, pages
2015. , 30 p.
Keyword [en]
Random Forest, GDP, Forecast, Growth, Data Mining, Autoregressive, Regression
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-243028OAI: oai:DiVA.org:uu-243028DiVA: diva2:785776
Subject / course
Statistics
Supervisors
Available from: 2015-02-09 Created: 2015-02-03 Last updated: 2015-02-09Bibliographically approved

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fulltext(592 kB)779 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf