Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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
A Note on the Trade-off between Direct and Indirect Seasonal Adjustments
Stockholm University, Faculty of Social Sciences, Department of Statistics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Direct and indirect seasonal adjustments can be viewed as opposite formulations of an error minimization problem that occurs when seasonally adjusting a system of time series. In this study, a loss function is formulated that is a weighted combination of the errors of the input time series and the aggregate error. Holt-Winters’ exponential smoothing methods on squared error loss functions or robust Huber loss functions are applied to quarterly Swedish GDP and monthly foreign trade data. All input series are seasonally adjusted jointly but still univariately and trade-off point between direct and indirect seasonal adjustments are estimated. The quadratic loss function is found to cause larger differences between direct and indirect seasonal adjustments than the Huber loss function does. Results indicate that pure indirect seasonal adjustment should be avoided for GDP and pure direct seasonal adjustment should be avoided for foreign trade. Adjustments in between with a combined loss function seem to work well for all purposes.

Keyword [en]
direct/indirect seasonal adjustment, Huber loss function, exponential smoothing, trade-off
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-89347OAI: oai:DiVA.org:su-89347DiVA: diva2:617325
Available from: 2013-04-22 Created: 2013-04-22 Last updated: 2013-04-29
In thesis
1. Seasonal Adjustment and Dynamic Linear Models
Open this publication in new window or tab >>Seasonal Adjustment and Dynamic Linear Models
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this framework to do seasonal adjustments of empirical and artificial data. A simple model and an extended model based on Gibbs sampling are used and the results are compared with the results of a standard seasonal adjustment method. The state space approach is then extended to discuss direct and indirect seasonal adjustments. This is achieved by applying a seasonal level model with no trend and some specific input variances that render different signal-to-noise ratios. This is illustrated for a system consisting of two artificial time series. Relative efficiencies between direct, indirect and multivariate, i.e. optimal, variances are then analyzed. In practice, standard seasonal adjustment packages do not support optimal/multivariate seasonal adjustments, so a univariate approach to simultaneous estimation is presented by specifying a Holt-Winters exponential smoothing method. This is applied to two sets of time series systems by defining a total loss function that is specified with a trade-off weight between the individual series’ loss functions and their aggregate loss function. The loss function is based on either the more conventional squared errors loss or on a robust Huber loss. The exponential decay parameters are then estimated by minimizing the total loss function for different trade-off weights. It is then concluded what approach, direct or indirect seasonal adjustment, is to be preferred for the two time series systems. The dynamic linear modeling approach is also applied to Swedish political opinion polls to assert the true underlying political opinion when there are several polls, with potential design effects and bias, observed at non-equidistant time points. A Wiener process model is used to model the change in the proportion of voters supporting either a specific party or a party block. Similar to stock market models, all available (political) information is assumed to be capitalized in the poll results and is incorporated in the model by assimilating opinion poll results with the model through Bayesian updating of the posterior distribution. Based on the results, we are able to assess the true underlying voter proportion and additionally predict the elections.

Place, publisher, year, edition, pages
Stockholm: Department of Statistics, Stockholm University, 2013. 8 p.
Keyword
Dynamic linear models, DLM, direct and indirect seasonal adjustment, relative efficiency, Huber loss function, Polls of polls, Wiener process, Swedish elections
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:su:diva-89496 (URN)978-91-7447-678-1 (ISBN)
Public defence
2013-06-12, DeGeersalen, Geovetenskapens hus, Svante Arrhenius väg 14, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of doctoral defence the following papers were unpublished and had a status as follows: Paper 3: Manuscript; Paper 4: Manuscripts

Available from: 2013-05-16 Created: 2013-04-27 Last updated: 2013-04-29Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Tongur, Can
By organisation
Department of Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 62 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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