Robust tests for white noise and cross-correlation
Robust tests for white noise and cross-correlation
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 nonstationarities 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.
Dalla, Violetta
1b585748-21e3-47f3-b46d-2f8a1819c776
Giraitis, Luidas
7e9ad928-e548-40e9-a529-ae1696077044
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Dalla, Violetta
1b585748-21e3-47f3-b46d-2f8a1819c776
Giraitis, Luidas
7e9ad928-e548-40e9-a529-ae1696077044
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Dalla, Violetta, Giraitis, Luidas and Phillips, Peter Charles Bonest
(2020)
Robust tests for white noise and cross-correlation.
Econometric Theory.
(doi:10.1017/S0266466620000341).
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 nonstationarities 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.
Text
revised_paper_ET 4189_27_03_20
- Accepted Manuscript
More information
Accepted/In Press date: 18 May 2020
e-pub ahead of print date: 21 September 2020
Additional Information:
Publisher Copyright:
© 2020 Cambridge University Press.
Identifiers
Local EPrints ID: 444579
URI: http://eprints.soton.ac.uk/id/eprint/444579
ISSN: 0266-4666
PURE UUID: ab34c412-7d81-47f2-b2f4-90ac7a33cd16
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Date deposited: 26 Oct 2020 17:32
Last modified: 16 Mar 2024 09:38
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Author:
Violetta Dalla
Author:
Luidas Giraitis
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