Evaluation and comparison of GDP growth and inflation forecasts for Portugal: National and international organizations

• Autor(a) principal: Moreira, Inês Alexandra Marques
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/26823
• topic: Previsão económica -- Economic forecast; Forecast evaluation; Combination of forecasts; Portugal; Avaliação de previsões; Combinação de previsões

This dissertation focus, firstly, on evaluating the forecast accuracy of the macroeconomic variables, GDP growth and inflation, issued for Portugal for the current year, 6 months ahead, 1 year ahead and 18 months ahead. Regarding the period of the analysis, this paper studies a longer period (2002-2021), which includes forecasts from Central Bank of Portugal, Ministry of Finance, European Commission, Organization for Economic Cooperation and Development and International Monetary Fund and a shorter period (2016-2021), that also includes the Portuguese Public Finance Council. To evaluate and compare the accuracy of the forecasts, summary statistics are computed, namely the mean forecast error, the mean absolute forecast error and root mean squared forecast error. Moreover, tests for unbiasedness and efficiency are performed to assess whether the forecasts are optimal. By using the Diebold Mariano test and a modified version proposed later, the difference in accuracy between institutions is evaluated. Secondly, the goal is to combine the forecasts and analyze if the combined forecasts are better than the individual institutions’ forecasts. The results suggest that, in general, the Central Bank of Portugal is the best institution, especially for the longer period. The results for the shorter period are more inconsistent, due to the small number of observations. In most cases, forecasts are unbiased and efficient. Regarding the combination of forecasts, the average of all institutions has a better performance than the majority of single institutions.