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This paper examines the distribution fu nctions of elasticity estimators in translog demand models. The authors consider the normaland ratio-of-normals distributions and present confidence intervals for the elasticity estimators. The results suggest that only elasticity estimato rs based on the means of the actual cost shares are likely to follow either the normal or ratio-of-normals distribution function. Examination of three published empirical studies demonstrates that…

Hausman、Ramsey 与 Chow 设定检验之间的关系AI 解读

This article describes a relationship between the tests for specification error in regression models introduced by Ramsey (1969) and Hausman (1978). It also shows that the Chow (1960) test for structural shift can be viewed as a special case of specification error tests. This article shows the common structure of these tests. The common basis is the comparison of alternative estimators of regression coefficients. Under the null hypothesis, one estimator is efficient and…

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不同模型设定误差检验的比较研究AI 解读

This article compares the power of the test RESET to that of a number of autocorrelation tests in detecting the errors of omitted variables or incorrect functional form in regression analysis. The autocorrelation tests considered are the Durbin-Watson test and a chi-squared test on the first H autocorrelations in the residual vector. Monte Carlo experiments reveal that the RESET test is the most powerful test for detecting specification errors and is robust to…

替代性模型设定误差检验：一项比较研究AI 解读

This article compares the power of the test RESET to that of a number of autocorrelation tests in detecting the errors of omitted variables or incorrect functional form in regression analysis. The autocorrelation tests considered are the Durbin-Watson test and a chi-squared test on the first H autocorrelations in the residual vector. Monte Carlo experiments reveal that the RESET test is the most powerful test for detecting specification errors and is robust to…

线性回归模型设定误差检验的若干性质AI 解读

This article considers the test reset which is intended to detect a nonzero mean of the disturbance in a linear regression model. Analysis of an approximation to the test statistic's distribution and Monte Carlo experiments reveal that the power of the test may decline as the size of the disturbance mean increases. However, the possibility is remote and declines with increasing sample size. Alternative sets of test variables are considered, and their effect on the power of…