Predicting business cycles with linear and non-linear filters

• Autor(a) principal: Abrantes, Beatriz
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10400.14/45823
• topic: Business cycles; Linear filters; Non-linear filters; Trend-cycle decomposition; Time series; Ciclos económicos; Filtros lineares; Filtros não lineares; Decomposição ciclo-tendência; Séries temporais

Business cycles represent the short-run fluctuations in economies and have a non recurring periodic character that makes them difficult to forecast. This dissertation fo cuses on the cycle-trend decomposition techniques that are used to remove the long-run component and thus obtain the cyclical component of macroeconomic series. Statistical filters can be used for this purpose, and through them, this work aims to clarify and visualize the cycle-trend decomposition. The primary objective of this dissertation is to evaluate the performance of two types of filters, linear and non-linear. At the end, it is also expected that conclusions will be drawn about the tool used throughout this work, Power BI. After comparing the linear filter developed by Hodrick and Prescott (1997) with two non-linear filters, MR filter and median filter developed by Mosheiov and Raveh (1997) and Wen and Zeng (1999), respectively, the results obtained were favorable compared to the non-linear filter. The MR filter proved to be able to produce a more robust trend than the others and to identify economic periods in a natural way. The MED filter proved to be able to produce less volatile and noisy cyclical components than the others; this is due to its ability to capture sharp changes in the trend and suppress them in the cyclical component. This concluded that the nonlinear filters performed well against the linear filter under study. Power BI demonstrated throughout the work several capabilities that characterize it as a good Business Intelligence tool, however, with room for improvement.