A Non-standard Empirical Likelihood for Time Series

Daniel J. Nordman (), Helle Bunzel and Soumendra N. Lahiri ()
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Daniel J. Nordman: Department of Statistics, Iowa State University, Postal: Snedecor Hall Ames IA 50011-1210, USA
Soumendra N. Lahiri: Department of Statistics, Texas A&M University, Postal: 3143 TAMU, College Station, TX 77843-3143, USA

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new version of BEL based on a simple, though non-standard, data-blocking rule which uses a data block of every possible length. Consequently, the method involves no block selection and is also anticipated to exhibit better coverage performance. Its non-standard blocking scheme, however, induces non-standard asymptotics and requires a significantly different development compared to standard BEL. We establish the large-sample distribution of log-ratio statistics from the new BEL method for calibrating confidence regions for mean or smooth function parameters of time series. This limit law is not the usual chi-square one, but is distribution-free and can be reproduced through straightforward simulations. Numerical studies indicate that the proposed method generally exhibits better coverage accuracy than standard BEL.

Keywords: Brownian motion; Confidence Regions; Stationarity; Weak Dependence (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 27
Date: 2012-12-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Working Paper: A Nonstandard Empirical Likelihood for Time Series (2013)
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