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Combination of sample surveys and projections of political opinions
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Stockholm University, Faculty of Social Sciences, Department of Statistics.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In Sweden, political party preferences are surveyed almost every month by several institutes. The sample sizes are usually between 1000 and 2000 individuals, which means that the standard deviations are between 1 and 1.5 %. We study how these estimates can be improved by combining them and by modelling the behaviour over time. Our model is a combination of a dynamic model based on Wiener processes and sampling theory with design effects and measurement biases. The variances of our estimates are about 1/3 of those of the original polls when only previous polls are used and about 1/5 if the information in later polls is included. The proposed method leads to a smaller bias since the institute biases can be estimated. The party preferences are modelled as random processes, making it possible to study the probability for events like a party (or block) getting more than 50 % of the political preferences. Assuming that the same model will hold in the future, we can present intervals for future election results.

National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-89314OAI: oai:DiVA.org:su-89314DiVA: diva2:617057
Available from: 2013-04-21 Created: 2013-04-21 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

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CiteExportLink to record
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Citation style
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