Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three-pass regression filter

摘要

Gross domestic product (GDP) is a key summary of macroeconomic conditions and it is closely monitored both by policy makers and by decision makers in the private sector. However, it is only available on a quarterly frequency, and in many countries it is released with a substantial delay. There are, however, many higher frequency and more timely economic and financial indicators that could be used for nowcasting and short-term forecasting GDP. Against this backdrop, we propose a modification of the three-pass regression filter to make it applicable to large mixed frequency data sets with ragged edges in a forecasting context. The resulting method, labelled MF-3PRF, is very simple but compares well with alternative mixed frequency factor estimation procedures in terms of theoretical properties and actual GDP nowcasting and forecasting for the USA and a variety of other countries.