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Essays on Data-driven, Non-parametric Modelling of Time-series
Hanus, Luboš ; Vácha, Lukáš (advisor) ; Witzany, Jiří (referee) ; Ellington, Michael (referee) ; Trimborn, Simon (referee)
This thesis consists of four contributions to the literature on data-driven and non-parametric modelling of time series. In the first paper, we study the synchronisation of business cycles and propose a multivariate co-movement measure based on time-frequency cohesion. We suggest that economic inte- gration may lead to increased co-movement of business cycles, which may reflect the benefits of convergence and coordination of economic policies. The second paper presents a new methodology for identifying persistence in macroeconomic variables. Using time-varying frequency response func- tions, we identify heterogeneous persistence effects in US macroeconomic variables. The third and fourth papers propose data-driven techniques for probabilistic forecasting of time series using deep learning. We introduce a multi-output neural network that selects the most appropriate distribution for the data. The distributional neural network is valuable for modelling data with non-linear, non-Gaussian and asymmetric structures. The third paper demonstrates the usefulness of the method by estimating information-rich macroeconomic fan charts and distributional forecasts of asset returns. In the last paper, we present the distributional neural network to obtain the proba- bility distribution of electricity price...
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