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Testing for Symmetry in Weakly Dependent Time Series

Luke Hartigan ()

No 2016-18, Discussion Papers from School of Economics, The University of New South Wales

Abstract: I propose a test of symmetry for a stationary time series based on the difference between the dispersion above the central tendency of the series with that below it. The test has many attractive features: it is applicable to dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null of symmetry. The finite sample properties of the test are examined via Monte Carlo simulation and suggest that it is more powerful than competing tests in the literature for the DGPs considered. I apply the test to investigate business cycle asymmetry in sectoral data and confirm previous findings that asymmetry is more often detected in goods-producing sectors than service-related sectors.

Keywords: Symmetry; Weak dependence; Hypothesis testing; Monte Carlo simulation; Business cycle asymmetry (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 C52 E32 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2016-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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