Working Paper: CEPR ID: DP6708
Authors: Massimiliano Marcellino; Christian Schumacher
Abstract: 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 sometimes 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 nowcast 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
Keywords: Business Cycle; Large Factor Models; MIDAS; Missing Values; Mixed-Frequency Data; Nowcasting
JEL Codes: C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| ragged-edge data (C55) | nowcasting accuracy of GDP (E01) |
| EM algorithm combined with PCA (C38) | nowcasting accuracy of GDP (E01) |
| midas projection method (E17) | nowcasting accuracy of GDP (E01) |
| factor estimation methods (C51) | nowcasting performance (C53) |
| ragged-edge data (C55) | performance of nowcasting models (C53) |