umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
GDP forecasting and nowcasting: Utilizing a system for averaging models to improve GDP predictions for six countries around the world
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This study was issued by Swedbank because they wanted too improve their GDP growth forecast capabilites.  A program was developed and tested on six countries; USA, Sweden, Germany, UK, Brazil and Norway.

In this paper I investigate if I can reduce forecasting error for GDP growth by taking a smart average from a variety of models compared to both the best individual models and a random walk. I combine the forecasts from four model groups: Vector autoregression, principal component analysis, machine learning and random walk. The smart average is given by a system that give more weight to the predictions of models with a lower historical error. Different weighting schemas are explored; how far into the past should we look? How much should bad performance be punished?

I show that for the six countries studied the smart average outperforms the single best model and that for five out of six countries it beats a random walk by at least 25%.

Abstract [sv]

Den här studien beställdes av Swedbank eftersom de ville förbättra sin BNP-prediktionsförmåga. Ett dataprogram utvecklades och testades på sex länder; USA, Sverige, Tyskland, Storbritannien, Brasilien och Norge.

I den här rapporten undersöker jag om jag kan minska felmarginalen för BNP-utvecklingsprognoser genom att ta ett smart genomsnitt från flera olika modeller jämfört med både den bästa individuella modellen och en random walk. Jag kombinerar prognoser från fyra modellgrupper: Vektor autoregression, principalkomponentanalys, maskininlärning och random walk. Det smarta genomsnittet skapas genom att ge mer vikt till de modeller som har lägst historiskt felmarginal. Olika viktningsscheman utforskas; hur långt bak i tiden ska vi mäta? Hur hårt ska dåliga prediktioner bestraffas?

Jag visar att för de sex länderna i studien presterar det smarta genomsnittet bättre än den enskilt bästa modellen och fem av de sex länderna slår en random walk med mer än 25%.

Place, publisher, year, edition, pages
2017. , 37 p.
Keyword [en]
Forecast evaluation; Nowcast; GDP; Vector autoregression; Machine learning; System for averaging models
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-131718OAI: oai:DiVA.org:umu-131718DiVA: diva2:1075467
External cooperation
Swedbank
Educational program
Master of Science in Engineering and Management
Presentation
2017-02-15, MA356, Mithuset, Umeå Universitet, 901 87, Umeå, 09:08 (Swedish)
Supervisors
Examiners
Available from: 2017-02-27 Created: 2017-02-20 Last updated: 2017-02-27Bibliographically approved

Open Access in DiVA

fulltext(3119 kB)80 downloads
File information
File name FULLTEXT01.pdfFile size 3119 kBChecksum SHA-512
4b948eddd6cdfbc75044543535e8ad8c67324e0e1d1c8b4bd833482d3ad8c12e684379549e40ac9413a98f0b322543a5845f4144b7158b56d0db88b3f354d29d
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Lundberg, Otto
By organisation
Department of Mathematics and Mathematical Statistics
Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 80 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 549 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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