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Robust Tests for White Noise and Cross-Correlation

Violetta Dalla, Liudas Giraitis and Peter Phillips ()
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Violetta Dalla: National and Kapodistrian University of Athens
Liudas Giraitis: Queen Mary, University of London

No 906, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: 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 unaffected 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 (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Date: 2020-05-27
New Economics Papers: this item is included in nep-ore and nep-sea
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Related works:
Working Paper: Robust Tests for White Noise and Cross-Correlation (2020) Downloads
Working Paper: Robust Tests for White Noise and Cross-Correlation (2019) Downloads
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