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MIDAS: Forecasting quarterly GDP using higher-frequency data
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]

We forecast US GDP sampled quarterly over horizons ranging from one quarter to three years. Using AR-MIDAS models we study three lag polynomials: the Almon lag, the exponential Almon lag and the beta lag, and nine macroeconomic variables, sampled weekly or monthly. Our benchmark model is an AR(1) and we compare forecast errors using RMSE. In all instances the AR-MIDAS achieves lower forecast errors compared to the benchmark model. The predictor sampled weekly generally performs better compared to other predictors, which are sampled monthly.

Place, publisher, year, edition, pages
2015. , 18 p.
Keyword [en]
MIDAS, GDP, forecasting, mixed-frequency data
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-242554OAI: oai:DiVA.org:uu-242554DiVA: diva2:783891
Subject / course
Statistics
Educational program
Freestanding course
Supervisors
Examiners
Available from: 2015-02-09 Created: 2015-01-27 Last updated: 2015-02-09Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • 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