2007): “Factor nowcasting of German GDP with ragged-edge data. A model comparison using MIDAS projections

This paper compares di¤erent ways to estimate the current state of the econ-omy using factor models that can handle unbalanced datasets. Due to the di¤erent release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the ragged edge of the data. Using a large monthly dataset of the German economy, we compare the performance of di¤erent factor models in the presence of the ragged edge: sta-tic and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are then used to estimate current quarter GDP, called the nowcast, using di¤erent versions of what we call factor-based mixed-data sam-pling (FACTOR-MIDAS) approaches. We compare all possible combinations of factor estimation methods and FACTOR-MIDAS projections with respect to now-cast performance. Additionally, we discuss the relevance of the missing observations at the end of the sample by comparing forecasts based on ragged-edge data with forecasts based on arti\u85cially balanced datasets. Finally, we compare the two-step FACTOR-MIDAS approach to nowcasts with a fully integrated state-space model