Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP*

Abstract

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed-frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high-and lowfrequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low-frequency variables like gross domestic product (GDP) exploiting information in a large set of higher-frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection Å The authors are grateful for helpful comments and discussions to three anonymous referees, Riccardo and Gerhard Rünstler. JEL Classification numbers: E37, C53.Factor MIDAS 519 methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real-time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state-space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed-frequency techniques that can exploit timely information from business cycle indicators.

Bottom-up or direct? Forecasting German GDP in a data-rich environment

“…An alternative framework has been proposed by Ghysels et al (2004); Andreou et al (2011) and has been recently applied by Clements and Galvão (2009) and Marcellino and Schumacher (2010) to macroeconomic forecasting. We follow their procedure which is called MIxed DAta Sampling (henceforth MIDAS) regression models and is meant to circumvent the problems of quarterly conversion.…”

Central Bank Macroeconomic Forecasting during the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

“…Applications of mixed frequency models to nowcast GDP growth in the U.S. or euro area include Clements and Galvão (2008), Clements andGalvão (2009), Marcellino andSchumacher (2010), Kuzin, Marcellino, and Schumacher (2011), Angelini, Camba-Mendez, Giannone, Reichlin, and Rünstler (2011), Andreou, Ghysels, and Kourtellos (2013, Kuzin, Marcellino, and Schumacher (2013) among others. The general finding is that these nowcasting models generally outperform models using quarterly frequency only, and are comparable to judgmental forecasts.…”

Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland

“…9 In doing so, we treat this indicators in a pseudo real-time framework similar to (Drechsel and Scheufele, 2012) and (Marcellino and Schumacher, 2010), for example, who is even more restrictive then us by using final data vintages not only for economic indicators but also for time series they forecast like industrial production and GDP, respectively. We, however, chose to retain the real-time aspect of forecasting as much as data availability allows us.…”