Methods for Pastcasting, Nowcasting and Forecasting Using Factor-MIDAS: With an Application to Korean GDP

Abstract

We utilize mixed frequency factor-MIDAS models for the purpose of carrying out pastcasting, nowcasting, and forecasting experiments using real-time data. We also introduce a new real-time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor-MIDAS prediction models. Our key empirical findings are that: (i) When using real-time data, factor-MIDAS prediction models outperform various linear benchmark models. Interestingly, the MSFE-best MIDAS models contain no AR lag terms when pastcasting and nowcasting. AR terms only begin to play a role in true forecasting contexts. (ii) Models that utilize only 1 or 2 factors are MSFE-best at all forecasting horizons, but not at any pastcasting and nowcasting horizons. In these latter contexts, much more heavily parameterized models with many factors are preferred. (iii) Real-time data are crucial for forecasting Korean GDP, and the use of first available versus most recent data strongly affects model selection and performance. (iv) Recursively estimated models are almost always MSFE-best, and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our MSFE-best factor-MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.