Economic growth forecasting in Nordic countries : a comparative analysis between generalized autoregressive conditional heteroscedasticity models and nonlinear autoregressive neural network models

• Main Author: Kauppinen, Markus
• Other Authors: Lappeenrannan-Lahden teknillinen yliopisto LUT, Lappeenranta-Lahti University of Technology LUT
• Format: Master Thesis
• Language: English
• Published: 2021
• Subjects: NAR; ARMA-GARCH; ANN; GDP; economic growth forecasting; comparative analysis; talouskasvun ennustaminen; vertaileva analyysi; fi=Datatiede|en=Data science|; fi=Liiketalous|en=Business economics|; fi=School of Business and Management; Kauppatieteet|en=School of Business and Management; Business Administration|; Norway; Iceland
• Online Access: https://lutpub.lut.fi/handle/10024/163077

Advanced computational efficiency has made possible to utilize artificial neural networks (ANN) in economic forecasting. This thesis studies differences between hybrid autoregressive moving average with generalized autoregressive conditional heteroscedastic (ARMA-GARCH) -models and nonlinear autoregressive neural network (NAR) - models in predicting quarterly real growth rate of GDP in five Nordic countries. Models are fitted to the in-the-sample subsample derived from the original time series data sample and values for next 20 timesteps are predicted and compared with the actual out-of-sample values derived from original time series data. Results indicate that there are slight differences in predicting capability among the models. In general, as a whole NAR -models could not outperform ARMA-GARCH -models. In case of Iceland NAR -models produced more accurate forecasts than ARMA-GARCH models and in case of Denmark NAR -models did perform better than the majority of ARMA-GARCH -models. For Norway and Sweden, the results are mixed; neither NAR -models nor ARMA-GARCH -models outperformed each other clearly, but NAR -models performed slightly worse in general. For Finland the results could not be derived due to characteristics of the time series data. Kehittynyt laskentateho on mahdollistanut keinotekoisten neuroverkkojen hyödyntämisen talouskasvun ennustamisessa. Tämä Pro Gradu -tutkielma tutkii eroavaisuuksia yhdistettyjen autoregressiivisten liukuvan keskiarvon ja yleistettyjen autoregressiivisien ehdollisesti heteroskedastisien -mallien (ARMA-GARCH), sekä epälineaarisien autoregressiivisten neuroverkkomallien (NAR) välillä kvartaalittaisen aidon talouskasvun ennustamisessa Pohjoismaissa. Mallit sovitetaan sisäiseen dataotokseen, joka on johdettu alkuperäisestä aikasarjadatasta, jonka jälkeen ennustetaan arvot 20:lle seuraavalle aika-arvolle. Ennustettuja arvoja verrataan alkuperäisestä aikasarjadatasta muodostetun vertailudatan kanssa. Tutkimuksen tulokset osoittavat, että mallien välillä on hienoja ...