Forecasting Methods Applied to Macroeconomic Variables
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School of Business |
Bachelor's thesis
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Authors
Date
2021
Department
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Mcode
Degree programme
Taloustiede
Language
en
Pages
28 + 10
Series
Abstract
This thesis studies macroeconomic forecasting using time series models. The precise research question is how effective autoregressive integrated moving average (ARIMA) and vector autoregression (VAR) models are in predicting Finland’s GDP. To answer this question, a relevant literature review and theoretical framework regarding ARIMA and VAR models in time series forecasting are provided after which an empirical study is conducted using R programming language. In the empirical work the ARIMA model uses GDP and the VAR model GDP, unemployment and inflation data of Finland from 1998 to 2020. The results show that both models overestimate the GDP of Finland in 2020 by a large margin when trained with sub- sets of the data. This overestimation of the models is expected due to the impact of the COVID-19 pandemic to the global economy in 2020. When trained with all of the available data, the ARIMA model forecasts GDP growth of 3,1% and 2,7% for 2021 and 2022, respectively. Corresponding results for the VAR model are 3,3% and 2,6%. These forecasts, although slightly overestimated, are in line with the official forecasts by the European Commission. This leads to the conclusion that the ARIMA and VAR models are viable forecasting tools to predict Finland’s GDP.Description
Thesis advisor
Ilmakunnas, PekkaKeywords
macroeconomic forecasts, ARIMA, VAR, GDP, inflation, unemployment, growth