Structural time series models in GDP analysis

Abstract

The aim of this thesis is to use structural time series models to estimate the business cycles of Estonia and its five key trading partners, with additional objectives of examining potential economic dependencies between the estimated cycles and evaluating the forecast accuracy of structural models. To address these research questions, structural models with both trigonometric and ARMA cycle formulations were applied to GDP time series data, alongside an alternative cycle estimation method of the Hodrick-Prescott (HP) filter. The resulting cycle estimates were further used to test Granger causality. Additionally, ARIMA models were estimated for comparative purposes in forecasting evaluation. The thesis includes a brief overview of gross domestic product (GDP) and business cycles, a comprehensive overview of the applied methods, a summary of relevant previous research, and the results of the analysis. As a result of this thesis, business cycles were successfully estimated for all the countries considered in the analysis, economic dependencies between Estonia and its trading partners were identifies, and the forecast accuracy of the models was assessed.