Change search
ReferencesLink to record
Permanent link

Direct link
Forecasting the Business Cycle using Partial Least Squares
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Prediktion av ekonomiskacykler med hjälp av partiella minsta kvadrat metoden (Swedish)
Abstract [en]

Partial Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly correlated) variables. While being a well established modeling tool in chemometrics, this thesis adapts PLS to financial data to predict the movements of the business cycle represented by the OECD Composite Leading Indicators. High-dimensional data is used, and a model with automated variable selection through a genetic algorithm is developed to forecast different economic regions with good results in out-of-sample tests.

Abstract [sv]

Partial Least Squares är både en regressionsmetod och ett verktyg för variabelselektion som är specielltlämpligt för modeller baserade på en stor mängd (möjligtvis korrelerade) variabler.Medan det är en väletablerad modelleringsmetod inom kemimetri, anpassar den häruppsatsen PLS till finansiell data för att förutspå rörelserna av konjunkturen,representerad av OECD's Composite Leading Indicator. Högdimensionella dataanvänds och en model med automatiserad variabelselektion via en genetiskalgoritm utvecklas för att göra en prognos av olika ekonomiska regioner medgoda resultat i out-of-sample-tester

Place, publisher, year, edition, pages
TRITA-MAT-E, 2014:58
Keyword [en]
Quantitative Forecast, Partial Least Squares, Variable Selection, High-dimensional Regression, Big Data, Business Cycle, Leading Indicators
National Category
Mathematical Analysis
URN: urn:nbn:se:kth:diva-151378OAI: diva2:749657
Subject / course
Educational program
Master of Science - Mathematics
Available from: 2014-09-24 Created: 2014-09-18 Last updated: 2014-09-24Bibliographically approved

Open Access in DiVA

fulltext(1037 kB)90 downloads
File information
File name FULLTEXT01.pdfFile size 1037 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Mathematical Analysis

Search outside of DiVA

GoogleGoogle Scholar
Total: 90 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: 192 hits
ReferencesLink to record
Permanent link

Direct link