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A self-normalizing approach to the specification test of mixed-frequency models

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  • Henriette Groenvik
  • Yeonwoo Rho

Abstract

In econometrics and finance, variables are collected at different frequencies. One straightforward regression model is to aggregate the higher frequency variable to match the lower frequency with a fixed weight function. However, aggregation with fixed weight functions may overlook useful information in the higher frequency variable. On the other hand, keeping all higher frequencies may result in overly complicated models. In literature, mixed data sampling (MIDAS) regression models have been proposed to balance between the two. In this article, a new model specification test is proposed that can help decide between the simple aggregation and the MIDAS model.

Suggested Citation

  • Henriette Groenvik & Yeonwoo Rho, 2018. "A self-normalizing approach to the specification test of mixed-frequency models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(8), pages 1913-1922, April.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:8:p:1913-1922
    DOI: 10.1080/03610926.2017.1332222
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    Cited by:

    1. Yun Liu & Yeonwoo Rho, 2018. "On the Choice of Instruments in Mixed Frequency Specification Tests," Papers 1809.05503, arXiv.org.

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