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MIDAS regression: a new horse in the race of filtering macroeconomic time series

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  • Michal Bencik

    (National Bank of Slovakia)

Abstract

We propose a new method of dealing with the end point problem when filtering economic time series. The main idea is to replace filtered quarterly observations at the end of the sample with static forecasts from a MIDAS regression using higher frequency time series. This method is capable to improve stability of output gap estimates or other cyclical series, as we confirm by empirical analysis on selected CEE countries and the United States. We find that stability may still be violated due to structural breaks in business cycles, or by an excessive amount of short-term noise. While MIDAS regressions have the potential to improve output gap estimates compared to the HP filter approach, the country-specific circumstances play a considerable role and need to be considered.

Suggested Citation

  • Michal Bencik, 2023. "MIDAS regression: a new horse in the race of filtering macroeconomic time series," Working and Discussion Papers WP 8/2023, Research Department, National Bank of Slovakia.
    • Handle: RePEc:svk:wpaper:1100
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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