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Forecast mean squared error reductionin the VAR(1) process
Försäkringskassan.
Högskolan i Jönköping.
2009 (English)In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 36, no 12, 1369-1384 p.Article in journal (Refereed) Published
Abstract [en]

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.

Place, publisher, year, edition, pages
Taylor & Francis, 2009. Vol. 36, no 12, 1369-1384 p.
Keyword [en]
VAR models, prediction error, pre-test, linear hypothesis, selection criteria
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:lnu:diva-28561DOI: 10.1080/02664760802715898OAI: oai:DiVA.org:lnu-28561DiVA: diva2:643520
Available from: 2013-08-27 Created: 2013-08-27 Last updated: 2017-12-06Bibliographically approved

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Holgersson, Thomas

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  • apa
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf