Alternative forms and properties of the score test

Bera, Anil K.; McKenzie, Colin

doi:10.1080/02664768600000002

Cited by 38 publications

(14 citation statements)

References 16 publications

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“…In terms of notation, the matrices of explanatory variables are (4a) and (4b) Davidson and MacKinnon (1983), Kiviet (1986), and Bera and McKenzie (1986). In assessing this evidence Davidson and MacKinnon (1993, p. 190) conclude: "Based partly on theory and evidence, then, and partly on the convenience of using the same form of test for Gauss-Newton regressions as would normally be used with genuine regressions, we therefore recommend using the F test .…”

Section: The Gauss-newton Regressionmentioning

confidence: 95%

GNR, MGR, and exact misspeclfication testing

Stewart

2000

Econometric Reviews

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The Gauss-Newton regression (GNR) is widely used to compute Lagrange multiplier statistics. A regression described by Milliken and Graybill yields an exact F test in a certain class of nonlinear models which are linear under the null. This paper shows that the Milliken-Graybill regression is a GNR. Hence one interpretation of Milliken-Graybill is that they identified a class of nonlinear models for which the GNR yields an exact test.

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Section: The Gauss-newton Regressionmentioning

confidence: 95%

GNR, MGR, and exact misspeclfication testing

Stewart

2000

Econometric Reviews

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“…In order to state some results in Section 2 we rely on asymptotic theory. In view of this, we planned Monte Carlo simulations to evaluate the empirical level and the power of the test statistics (16)- (19) at a nominal significance level of 5 per cent when testing hypothesis (20). Simulation settings are as follows.…”

Section: Simulation Studymentioning

confidence: 99%

Hypothesis testing in an errors‐in‐variables model with heteroscedastic measurement errors

Castro

Galea

Bolfarine

2008

Statistics in Medicine

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In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model's goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.

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“…10 10 Bera and MacKenzie (1986) has argued for the superior small‐sample performance of LM E over LM H and LM G , which has been found to be empirically supported. Also, the superior performance of LM D over LM H and LM G in small samples has been shown in many empirical studies (see Davidson and MacKinnon, 1983, 1984).…”

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confidence: 92%

Generalized LM tests for functional form and heteroscedasticity

Yang

Tse

2008

Econometrics Journal

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We present a generalized LM test of heteroscedasticity allowing the presence of data transformation and a generalized LM test of functional form allowing the presence of heteroscedasticity. Both generalizations are meaningful as non-normality and heteroscedasticity are common in economic data. A joint test of functional form and heteroscedasticity is also given. These tests are further "studentized" to account for possible excess skewness and kurtosis of the errors in the model. All tests are easy to implement. They are based on the expected information and are shown to possess excellent finite sample properties. Several related tests are also discussed and their finite sample performances assessed. We found that our newly proposed tests significantly outperform the others, in particular in the cases where the errors are non-normal. Copyright � 2008 The Author(s). Journal compilation � Royal Economic Society 2008

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Alternative forms and properties of the score test

Cited by 38 publications

References 16 publications

GNR, MGR, and exact misspeclfication testing

GNR, MGR, and exact misspeclfication testing

Hypothesis testing in an errors‐in‐variables model with heteroscedastic measurement errors

Generalized LM tests for functional form and heteroscedasticity

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