Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon...

In many macroeconomic forecasting applications factor models are used to cope with large datasets. This study aligns variational autoencoders with macroeconomic factor modeling and proposes an extension to adapt this framework for forecasting exercises. Variational autoencoders are well suited...

For many years, economists have been using statistical tools to estimate parameters of macroeconomic models. Forecasting plays a major role in macroeconomic planning and it is an essential analytical tool in countries' economic strategies. In recent years, researchers are developing new...

With the advent of Big Data and machine learning/AI technologies, actuaries can now develop advanced models in a data-rich environment to achieve better forecasting performance and provide added value in many applications. Traditionally, economic forecasting for actuarial applications is...