Forecasting the Real GDP of United Kingdom, Germany and France in Next Three Years

Gross Domestic Product (GDP) has been drawn much attention by people because GDP could show the finance situation of a country. Therefore, this study employs the Auto Regressive Integrated Moving Average (ARIMA) model to forecast the real GDP of United Kingdom, Germany, and France for the next three years (2025-2027). Utilizing quarterly real GDP data from 2015 to 2024, the paper identifies optimal ARIMA parameters () for each country and analyzes the implications of the forecasted trends. This study demonstrates the model's effectiveness in capturing economic trends through its interpretable structure and reliable differencing approach. The study also explores the limitations of ARIMA, such as its linearity assumption, and discusses the potential of integrating machine learning (ML) techniques to enhance forecasting accuracy in economic applications. The results indicate varying economic trajectories for the three nations, with policy and external factors like geopolitical events and post-pandemic recovery influencing outcomes. This work promises to be a reference for these countries.

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Publication Details

Journal
Theoretical and Natural Science
Published
2025-05-23
DOI
https://doi.org/10.54254/2753-8818/2025.gl23169
Primary Topic
German Economic Analysis & Policies
Type
article
Field-Weighted Citation Impact
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article

Forecasting the Real GDP of United Kingdom, Germany and France in Next Three Years

Dingshan Cao
Theoretical and Natural Science
German Economic Analysis & Policies
article

Forecasting the Real GDP of United Kingdom, Germany and France in Next Three Years

Dingshan Cao
article en

Abstract

Gross Domestic Product (GDP) has been drawn much attention by people because GDP could show the finance situation of a country. Therefore, this study employs the Auto Regressive Integrated Moving Average (ARIMA) model to forecast the real GDP of United Kingdom, Germany, and France for the next three years (2025-2027). Utilizing quarterly real GDP data from 2015 to 2024, the paper identifies optimal ARIMA parameters () for each country and analyzes the implications of the forecasted trends. This study demonstrates the model's effectiveness in capturing economic trends through its interpretable structure and reliable differencing approach. The study also explores the limitations of ARIMA, such as its linearity assumption, and discusses the potential of integrating machine learning (ML) techniques to enhance forecasting accuracy in economic applications. The results indicate varying economic trajectories for the three nations, with policy and external factors like geopolitical events and post-pandemic recovery influencing outcomes. This work promises to be a reference for these countries.

Theoretical and Natural ScienceVol. 109(1)
Openalex Percentile: Top 84%
German Economic Analysis & Policies
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