In econometrics, most null hypotheses are composite, dividing the parameters into parameters of interest and nuisance parameters. Typically, a composite hypothesis can be tested using two or more testing procedures. Competing testing procedures are commonly compared using size-corrected powers. What is often overlooked is that the size-corrected critical value of a test can be sensitive to the set of admissible values of the nuisance parameters, and hence its size-corrected power. As a result, different choices for the admissible set can produce different conclusions about which test is best. This fact complicates the interpretation of Monte Carlo power studies because in many cases there is no natural definition of the set of admissible values. We find this fact to be crucial when choosing a Lagrange Multiplier test in the case of a logit model. A theoretical explanation for this effect is developed using large parameter asymptotics.
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Paper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number
1997-17.
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
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