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Testing for Dependence in Non-gaussian Time Series Data

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journal contribution
posted on 03.11.2017, 00:16 authored by McCabe, B.P.M., Martin, Gael M., Freeland, R.K.
This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete, observations. Locally most powerful tests for various forms of dependence are derived, based on an approximate likelihood function. Invariance to the distribution adopted for the data, conditional on the latent process, is shown to hold in certain cases. The tests are applied to various financial data sets, and Monte Carlo experiments used to gauge their finite sample properties.

History

Classification-JEL

C12, C16, C22

Creation date

2004-06

Working Paper Series Number

13/04

Length

42 pages

File-Format

application/pdf

Handle

RePEc:msh:ebswps:2004-13