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Generalized empirical likelihood tests in time series models with potential identification failure

  • Patrik Guggenberger

    (Institute for Fiscal Studies)

  • Richard Smith

    ()

    (Institute for Fiscal Studies and University of Cambridge)

We introduce test statistics based on generalized empirical likelihood methods that can be used to test simple hypotheses involving the unknown parameter vector in moment condition time series models. The test statistics generalize those in Guggenberger and Smith (2005) from the i.i.d. to the time series context and are alternatives to those in Kleibergen (2001) and Otsu (2003). The main feature of these tests is that their empirical null rejection probabilities are not affected much by the strength or weakness of identification. More precisely, we show that the statistics are asymptotically distributed as chisquare under both classical asymptotic theory and weak instrument asymptotics of Stock and Wright (2000). A Monte Carlo study reveals that the finitesample performance of the suggested tests is very competitive.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0105.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP01/05.

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Length: 25 pp.
Date of creation: Apr 2005
Date of revision:
Handle: RePEc:ifs:cemmap:01/05
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