Improving the J test in the SARAR model by likelihood-based estimation
نویسنده
چکیده
It has been demonstrated recently that the empirical significance levels of the J-type tests introduced by Kelejian (2008) can be controlled in many cases by the use of a bootstrap to construct a reference distribution. When the spatial parameter estimates lie outside the invertibility region constructing bootstrap samples is problematic, however, and the present paper explores how far this practical obstacle may be removed by the use of likelihood-based moment conditions to construct the parameter estimates. The effects of different spatial weight patterns and sample size on the empirical significance levels and power of the tests are also investigated, with sample size found to be important but weight pattern less so.
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