Testing for Dependence in Non-Gaussian Time Series Data

Brendan McCabe, Gael Martin and R.K. Freeland

No 13/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: Latent variable model; locally most powerful tests; approximate likelihood; correlation tests; stochastic volatility tests. (search for similar items in EconPapers)
JEL-codes: C12 C16 C22 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2004-06
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: Testing for Dependence in Non-Gaussian Time Series Data (2004)
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