We develop a test statistic to test hypotheses in nonlinear, weighted regression models with serial correlation/heteroskedasticity of unknown form. The novel aspect is that these tests are simple and do not require use of heteroskedasticity autocorrelation consistent (HAC) estimators. Furthermore, they introduce a new class of test, utilizing stochastic transformations to eliminate nuisance parameters, as a substitute for estimating the nuisance parameters. We derive the limiting null distributions of these new tests in a general nonlinear setting, and show that while the tests have nonstandard distributions, the distributions depend only upon the number of restrictions. We apply this method of testing to an empirical example and illustrate that the size of the new test is less distorted than tests utilizing HAC estimators.
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