• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • Vis innførsel
  •   Hjem
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Machine Learning: Superior to Traditional Statistical Models in Forecasting Macroeconomic Time-Series?

Dorenberg, Helene Sophie; Moen, Margrethe Kristine
Master thesis
Thumbnail
Åpne
2940911.pdf (6.619Mb)
Permanent lenke
https://hdl.handle.net/11250/2824363
Utgivelsesdato
2021
Metadata
Vis full innførsel
Samlinger
  • Master of Science [1116]
Sammendrag
This thesis investigates machine learning's potential to forecast the

Norwegian GDP, unemployment rate, and in

ation on monthly or

quarterly, and annual terms. We compare machine learning techniques

such as penalised regressions and random forest to traditional

statistical methods such as the na ve model, autoregressive

and vector autoregressive models. This motivates the following

thesis question, Is value added by machine learning compared to

traditional statistical models in time{series forecasting of macroe-

conomic variables? The results show that the machine learning

models are relatively better than the traditional statistical models

when forecasting except for in

ation. Using many exogenous

variables to explain in

ation is more confusing than value{adding,

therefore, the models depending only on in

ation itself provide the

best forecasts.
Beskrivelse
This thesis investigates machine learning's potential to forecast the Norwegian GDP, unemployment rate, and in ation on monthly or quarterly, and annual terms. We compare machine learning techniques such as penalised regressions and random forest to traditional statistical methods such as the na ve model, autoregressive and vector autoregressive models. This motivates the following thesis question, Is value added by machine learning compared to traditional statistical models in time{series forecasting of macroe- conomic variables? The results show that the machine learning models are relatively better than the traditional statistical models when forecasting except for in ation. Using many exogenous variables to explain in ation is more confusing than value{adding, therefore, the models depending only on in ation itself provide the best forecasts.
Utgiver
Handelshøyskolen BI

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit