Neural networks can forecast economic data with accuracy matching that of conventional autoregressive methods such as SARIMA and VAR. This study uses dense, recurrent, convolutional, and convnet/RNN hybrids to conduct time-series analysis of interest rates, consumer and producer prices, and...

This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE)...

The good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved...