Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.

The paper analyses the degree of output persistence in GDP in order to empirically discriminate between the Solow growth model, the perfect competition endogenous growth model, the imperfect competition endogenous growth model, and the subcase of a multiple equilibria model of endogenous growth for the case of Austria. We find that a temporary shock in the growth rate of output induces a permanent and larger effect on the level of GDP. This leads us to refute the Solow growth model and the perfect competition model. We find strong empirical support for the imperfect competition growth model, but cannot fully rule out the possibility of multiple equilibria growth rates. / Series: Department of Economics Working Paper Series

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