Department of Economics and Business Economics

A Non-standard Empirical Likelihood for Time Series

Research output: Working paperResearch


  • Rp12 55

    Submitted manuscript, 456 KB, PDF document

  • Daniel J. Nordman, Iowa State University, United States
  • Helle Bunzel, Denmark
  • Soumendra N. Lahiri, Texas A&M University, United States
  • School of Economics and Management
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.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages27
Publication statusPublished - 4 Dec 2012
SeriesCREATES Research Papers

    Research areas

  • Brownian motion, Confidence Regions, Stationarity, Weak Dependence

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