Weak convergence of non-stationary multivariate marked processes with applications to martingale testing
This paper establishes the weak convergence of a class of marked empirical processes of possibly non-stationary and/or non-ergodic multivariate time series sequences under martingale conditions. The assumptions involved are similar to those in Brown's martingale central limit theorem. In particular, no mixing conditions are imposed. As an application, we propose a test statistic for the martingale hypothesis and we derive its asymptotic null distribution. Finally, a Monte Carlo study shows that the asymptotic results provide good approximations for small and moderate sample sizes. An application to the S&P 500 is also considered.
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Volume (Year): 98 (2007)
Issue (Month): 7 (August)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Joon Y. Park & Peter C.B. Phillips, 1998.
"Nonlinear Regressions with Integrated Time Series,"
Cowles Foundation Discussion Papers
1190, Cowles Foundation for Research in Economics, Yale University.
- Deo, Rohit S., 2000. "Spectral tests of the martingale hypothesis under conditional heteroscedasticity," Journal of Econometrics, Elsevier, vol. 99(2), pages 291-315, December.
- Velasco, Carlos & Escanciano, Juan Carlos, 2003.
"Generalized spectral tests for the martingale difference hypothesis,"
DES - Working Papers. Statistics and Econometrics. WS
ws035312, Universidad Carlos III de Madrid. Departamento de Estadística.
- Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
- Bierens, Herman J., 1982. "Consistent model specification tests," Journal of Econometrics, Elsevier, vol. 20(1), pages 105-134, October.
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