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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
    DOI: 10.1111/rssa.12363
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    Cited by:

    1. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
    2. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    3. Nicolas Chatelais & Menzie Chinn & Arthur Stalla-Bourdillon, 2022. "Macroeconomic Forecasting Using Filtered Signals from a Stock Market Cross Section," Working papers 903, Banque de France.
    4. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    5. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.

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