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An intuitive skewness-based symmetry test applicable to stationary time series data

Luke Hartigan ()

Studies in Nonlinear Dynamics & Econometrics, 2019, vol. 23, issue 5, 17

Abstract: I propose a simple skewness-based test of symmetry suitable for a stationary time series. The test is based on the difference between the squared deviation of a process above its median with that below it. The test has many attractive features: it is applicable to weakly dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null hypothesis of symmetry. The finite sample properties of the test statistic are examined via Monte Carlo simulation and suggest that it has better size-adjusted power compared to competing tests in the literature when examining moderately persistence processes. I apply the test to a range of US economic and financial data and find stronger support for asymmetry in financial series compared to economic series.

Keywords: hypothesis testing; Monte Carlo simulation; skewness; symmetry (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/snde-2017-0031

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