CFDP 2194

Robust Tests for White Noise and Cross-Correlation

Author(s): 

Publication Date: April 2019

Pages: 32

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.

Supplemental material

Supplement pages: 51

Keywords: Serial correlation, Cross-correlation, Heteroskedasticity, Martingale differences

JEL Classification Codes: C12

JEL Classification Codes: C12