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

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  • Christian Hepenstrick
  • Massimiliano Marcellino

Abstract

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.

Suggested Citation

  • Christian Hepenstrick & Massimiliano Marcellino, 2019. "Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three‐pass regression filter," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 69-99, January.
  • Handle: RePEc:bla:jorssa:v:182:y:2019:i:1:p:69-99
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    File URL: https://doi.org/10.1111/rssa.12363
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