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Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality

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  • Ghysels, Eric
  • Hill, Jonathan B.
  • Motegi, Kaiji

Abstract

This paper proposes a new test for a large set of zero restrictions in regression models based on a seemingly overlooked, but simple, dimension reduction technique. The procedure involves multiple parsimonious regression models where key regressors are split across simple regressions. Each parsimonious regression model has one key regressor and other regressors not associated with the null hypothesis. The test is based on the maximum of the squared parameters of the key regressors. Parsimony ensures sharper estimates and therefore improves power in small sample. We present the general theory of our test and focus on mixed frequency Granger causality as a prominent application involving many zero restrictions.

Suggested Citation

  • Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    • Handle: RePEc:eee:econom:v:218:y:2020:i:2:p:633-654
    DOI: 10.1016/j.jeconom.2020.04.032
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    References listed on IDEAS

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    2. Hong, Yanran & Xu, Pengfei & Wang, Lu & Pan, Zhigang, 2022. "Relationship between the news-based categorical economic policy uncertainty and US GDP: A mixed-frequency Granger-causality analysis," Finance Research Letters, Elsevier, vol. 48(C).

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    More about this item

    Keywords

    Dimension reduction; Granger causality test; Max test; Mixed Data Sampling (MIDAS); Parsimonious regression models;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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