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

Document Type

Article

Publication Date

4-18-2018

Abstract

© 2018 Taylor & Francis Group, LLC. 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.

Publication Title

Communications in Statistics - Theory and Methods

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