Forecasting economic growth with traditional methods and a simple neural network model

Macroeconomic time series forecasting is crucial for guiding government policy decisions, business strategies, and understanding economic trends. However, predicting macroeconomic variables remains a significant challenge. The complexity of economic systems, insufficient data, high levels of volatility complicate the task of accurate forecasting. To enhance forecasting accuracy, we propose two novel models to capture both linear and nonlinear dynamics. First, we generalize the random walk model by incorporating a drift term, which is estimated using a simple neural network model. Second, a hybrid model is introduced to combine local linear regression and the neural network model. Additionally, we adopt other models from Fritz et al. (2024) for combination. These models are combined using a simple averaging method. Our results demonstrate that the newly proposed neural network-based models produce the lowest average MASE. Additionally, model combination is an effective strategy for enhancing the performance of GDP forecasting in most countries and it is less risky than relying on a single model.