The Invariance of Score Tests to Measurement Error
نویسندگان
چکیده
The linear measurement error model is an alternative to the classical regression model, in which we assume that the independent variables are subject to error. This assumption can cause statistical inferences and parameter estimators to di er dramatically from those obtained from the classical regression model. However, inferences may remain unchanged even though the independent variables are assumed to be subject to error. This paper investigates the invariance property of score tests for assessing heteroscedasticity, rst-order autoregressive disturbance, and the need for a Box-Cox power transformation. Under speci c constraints, we show that the score tests for measurement error models are the same as the corresponding well-established tests derived from classical regression models. We also discuss some possible generalizations.
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