This project applies a univariate ARIMA model to forecast Germany’s Real GDP and evaluates its performance against random walk forecasts. By analyzing decades of economic data, this study highlights the power of time series modeling in economic forecasting.
Dataset and Scope
Analyzed quarterly Real GDP data for Germany from 1991Q1 to 2019Q4, sourced from the Federal Reserve Economic Data (FRED).
Model Development
Built an ARIMA (2, 0, 1) model after determining the optimal parameters using the Schwarz Information Criterion (SIC).
Conducted diagnostic tests to ensure model stability and accuracy.
Forecast Evaluation:
Generated one-quarter-ahead forecasts and compared them with actual values.
Demonstrated that ARIMA forecasts were unbiased, free from systematic bias, and outperformed random walk forecasts in terms of Mean Squared Error (MSE).
Key Insights:
While ARIMA forecasts contained similar predictive information to random walk forecasts, they proved significantly more accurate in MSE terms.
Recursive estimation techniques ensured forecasts remained robust as new data was added.
Why It Matters:
Accurate GDP forecasting is vital for policymakers, investors, and businesses to make informed decisions about economic planning, investment, and risk management. This project showcases the practical application of ARIMA modeling in understanding economic trends and improving forecasting accuracy.