TESTING FOR THE MARKOV PROPERTY IN TIME SERIES
By Bin Chen, Yongmiao Hong. Econometric Theory, 2011
60 citations recorded by the source corpus.
The Markov property is a fundamental property in time series analysis and is often assumed in economic and financial modeling. We develop a new test for the Markov property using the conditional characteristic function embedded in a frequency domain approach, which checks the implication of the Markov property in every conditional moment (if it exists) and over many lags. The proposed test is applicable to both univariate and multivariate time series with discrete or continuous distributions. Simulation studies show that with the use of a smoothed nonparametric transition density-based bootstrap procedure, the proposed test has reasonable sizes and all-around power against several popular non-Markov alternatives in finite samples. We apply the test to a number of financial time series and find some evidence against the Markov property.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/635a0d8d25b3f4df022b1957d3aa12cc1ad9c5db
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In Economics and Econometrics
- Liangjun Su
- Halbert White
- Maria Pitsillou
- Konstantinos Fokianos
- Dominic Edelmann
- Yiguo Sun
- Xun Lu
- Andrew J. Patton
In Finance
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- Dominic Edelmann
- Tzee-Ming Huang
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- Sessi Tokpavi
Citations in the corpus
- Testing Conditional Independence Via Empirical Likelihood (2014, methodology): “de Maros and Fernandes (2007) and Chen and Hong (2010) propose nonparametric tests for the Markov property (a special case of CI) based on the comparison of densities and generalized ∗We would like to express our appreciation to Qihui Chen for his outstanding research assistance.”
- An Updated Literature Review of Distance Correlation and Its Applications to Time Series (2017, background): “Chen and Hong (2012) developed a nonparametric test for the Markov property of a multivariate time series based on the conditional characteristic function. Based on the fact that the correlation coefficient is suitable for Gaussian data but it fails for nonlinear cases, many authors defined the corr”