Testing for Dependence in Non-Gaussian Time Series Data

Keith Freeland, Brendan McCabe and Gael Margaret Martin

No 313, Econometric Society 2004 Australasian Meetings from Econometric Society

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)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fin
Date: 2004-08-11
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http://repec.org/esAUSM04/up.24300.1078090666.pdf (application/pdf)

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