Challenges of Applying Neural Networks to Macroeconomic Data

Applying neural networks in macroeconomic analysis presents significant challenges due to the characteristics of macroeconomic data. This data often suffers from a small sample size, limiting the number of observations, and contains a wide array of variables such as inflation, unemployment, and GDP. Additionally, the temporal nature of such data can introduce issues like autocorrelation, complicating the ability to accurately capture relationships and non-linearities. The discussed research aims to address these challenges by utilizing Bayesian neural networks, which offer a flexible approach by incorporating a variety of activation functions to improve model performance.