Robust Tests for White Noise and Cross-Correlation
Liudas Giraitis and
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Violetta Dalla: National and Kapodistrian University of Athens
Liudas Giraitis: Queen Mary, University of London
No 2194, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University
Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung-Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate hidden dependence and non-stationarities involving heteroskedasticity, thereby uncoupling these tests from limiting assumptions that reduce their applicability in empirical work. To enhance the Ljung-Box test for non-i.i.d. data a new cumulative test is introduced. Asymptotic size of these tests is una"ected by hidden dependence and heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series. Tests for the i.i.d. property of a time series are also developed. An extensive Monte Carlo study confirms good performance in both size and power for the new tests. Applications to real data reveal that standard tests frequently produce spurious evidence of serial correlation.
Keywords: Serial correlation; Cross-correlation; Heteroskedasticity; Martingale differences (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 89 pages
Date: 2019-04, Revised 2020-03
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Note: Includes Supplimental Material
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Journal Article: ROBUST TESTS FOR WHITE NOISE AND CROSS-CORRELATION (2022)
Working Paper: Robust Tests for White Noise and Cross-Correlation (2020)
Working Paper: Robust Tests for White Noise and Cross-Correlation (2019)
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