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At the time of doctoral defence the following papers were unpublished and had a status as follows: Paper 3: Manuscript; Paper 4: Manuscripts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

The thesis titled Media and Pre-Election Polls: Quantitative Analysis of the Czech Major Daily Newspapers before Parliamentary Elections in 2010, deals with the manner in which the media grasp and process the results of opinion polls focused on the theme of elections, namely pre-election polls. The attitude of the author toward this topic is based on the agenda setting theory, which assumes that the media can, to some extent, set the topics and thus determine how (and that) the audience think about them. In the case of publication of pre- election surveys can therefore media, to some, but hardly detectable extent, influence the citizens-voters. For this reason it is important to focus on how exactly the mass media work with the statistic data and how they make them available to the wide public. Media coverage of these data should be complete and clear in order to prevent misinterpretation and for the data to be correctly handled. Through the quantitative content analysis of media outputs (in total 74 articles) from the five most popular Czech daily newspapers in the period January to May 2010 it was researched if the media work with pre-election polls results properly. Based on the analysis and its results, the recommendations were concluded for the situation to be improved.