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- W3123674818 abstract "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." @default.
- W3123674818 created "2021-02-01" @default.
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- W3123674818 date "2005-04-07" @default.
- W3123674818 modified "2023-09-25" @default.
- W3123674818 title "Generalized empirical likelihood tests in time series models with potential identification failure" @default.
- W3123674818 doi "https://doi.org/10.1920/wp.cem.2005.0105" @default.
- W3123674818 hasPublicationYear "2005" @default.
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