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Prediction of Linear Models: Application of Jackknife Model Averaging
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.

Place, publisher, year, edition, pages
2016. , 13 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-297671OAI: oai:DiVA.org:uu-297671DiVA: diva2:942839
Subject / course
Statistics
Educational program
Master Programme in Swedish
Supervisors
Available from: 2016-06-27 Created: 2016-06-27 Last updated: 2016-06-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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