This paper presents a novel dynamic factor model for non-stationary data. We begin by constructing a simple dynamic stochastic general equilibrium growth model and show that we can represent and estimate the model using a simple linear-Gaussian (Kalman) filter. Crucially, consistent estimation...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling...