Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1

This paper compares different ways to estimate the current state of the economy using factor
models that can handle unbalanced datasets. Due to the different release lags of business cycle
indicators, data unbalancedness often emerges at the end of multivariate samples, which is some-
times referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German
economy, we compare the performance of different factor models in the presence of the ragged edge:
static 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 used to estimate current quarter GDP, called the 'nowcast', using different versions of what
we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible
combinations of factor estimation methods and Factor-MIDAS projections with respect to now-
cast performance. Additionally, we compare the performance of the nowcast factor models with
the performance of quarterly factor models based on time-aggregated and thus balanced data,
which neglect the most timely observations of business cycle indicators at the end of the sample.
Our empirical findings show that the factor estimation methods don't differ much with respect
to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection
performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor
models that can exploit ragged-edge data.