Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP

Summary This paper provides a review of the recent literature concerned with large factor models as forecast devices.We focus on factor models that account for mixed-frequency data and missing observations at the end of the sample. These are data irregularities applied forecasters have to cope with in real time. To extract the factors from the irregular data, special factor estimation techniques are necessary, expanding on the standard approaches for balanced data such as principal components (PC). The estimation methods include variants of the Expectation-Maximisation (EM) algorithm together with PC and factor estimation using state-space models. Given the estimated factors, forecasts can be obtained from bridge equations, mixed-data sampling (MIDAS) regressions and the Kalman smoother applied to fully-fledged factor models in state-space form. Empirical applications for German GDP growth often find that forecasts based on factor models are informative only a few months ahead compared to naive benchmarks. Thus, these models can be regarded as short-term forecast tools only. However, the factor models estimated on mixed-frequency data with missing observations tend to outperform factor models based on balanced data time-aggregated from high-frequency data.