@article{Davidson:274643,
      recid = {274643},
      author = {Davidson, Russell and MacKinnon, James G.},
      title = {Bootstrap tests for overidentification in linear  regression models},
      address = {2014-04},
      number = {2110-2018-4453},
      series = {Working Paper No. 1318},
      pages = {42},
      year = {2014},
      abstract = {Little attention has been paid to the nite-sample  properties of tests for overidentifying restrictions in  linear regression models with a single endogenous regressor  and weak instruments. We study several such tests in models  estimated by instrumental variables (IV) and  limited-information maximum likelihood (LIML). Under the  assumption of Gaussian disturbances, we derive expressions  for a variety of test statistics as functions of eight  mutually independent random variables and two nuisance  parameters. The distributions of the statistics are shown  to have an ill-dened limit as the parameter that determines  the strength of the instruments tends to zero and as the  correlation between the disturbances of the structural and  reduced-form equations tends to plus or minus one.  Simulation experiments demonstrate that this makes it  impossible to perform reliable inference near the point at  which the limit is ill-dened. Several bootstrap procedures  are proposed. They alleviate the problem and allow reliable  inference when the instruments are not too weak. We also  study the power properties of the bootstrap tests.},
      url = {http://ageconsearch.umn.edu/record/274643},
      doi = {https://doi.org/10.22004/ag.econ.274643},
}