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基于统计量向量的假设检验AI 解读

This paper presents a new approach to hypothesis testing based on a vector of statistics. It involves simulating the statistics under the null hypothesis and then estimating the joint density of the statistics. This allows the p-value of the smallest acceptance region test to be estimated. We prove this p-value is a consistent estimate under some regularity conditions. The small-sample properties of the proposed procedure are investigated in the context of testing for…

多元核回归带宽选择的贝叶斯方法及其状态价格密度应用AI 解读

This paper presents a Bayesian approach to bandwidth selection for multivariate kernel regression. A Monte Carlo study shows that under the average squared error criterion, the Bayesian bandwidth selector is comparable to the cross-validation method and clearly outperforms the bootstrapping and rule-of-thumb bandwidth selectors. The Bayesian bandwidth selector is applied to a multivariate kernel regression model that is often used to estimate the state-price density of…

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ARCH 与 GARCH 回归扰动的局部最大平均功效得分检验AI 解读

This paper considers testing for autoregressive conditional heteroskedasticity and generalized autoregressive conditional heteroskedasticity disturbances in the linear regression model. These testing problems are one-sided in nature; a fact ignored by the Lagrange multiplier test. A test that exploits this one-sided aspect is constructed based on the sum of the scores. The size and power properties of two versions of this test under normal and leptokurtic disturbances are…

This article considers testing for first-order moving average against first-order autoregressive disturbances in the linear-regression model. Tests investigated include approximate point-optimal invariant (POI) tests, an asymptotic test of the second-order residual autocorrelation coefficient, and a Lagragne multiplier (LM) test. A Monte Carlo experiment compares their small-sample performances. Of the asymptotic tests, the LM test has the most satisfactory sizes, but its…

线性回归模型中移动平均扰动与自回归扰动的检验AI 解读

This article considers testing for first-order moving average against first-order autoregressive disturbances in the linear-regression model. Tests investigated include approximate point-optimal invariant (POI) tests, an asymptotic test of the second-order residual autocorrelation coefficient, and a Lagragne multiplier (LM) test. A Monte Carlo experiment compares their small-sample performances. Of the asymptotic tests, the LM test has the most satisfactory sizes, but its…

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Durbin–Watson 检验的局部最优性质AI 解读

Although originally designed to detect AR(1) disturbances in the linear-regression model, the Durbin-Watson test is known to have good power against other forms of disturbance behavior. In this paper, we identify disturbance processes involving any number of parameters against which the Durbin–Watson test is approximately locally best invariant uniformly in a range of directions from the null hypothesis. Examples include the sum of q independent ARMA(1,1) processes, certain…

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调查数据回归模型中的区组效应检验AI 解读

This article considers the problem of testing for intrablock or intracluster correlation in regression disturbances that may occur when cluster or two-stage sampling data is used in regression analysis. It points out that the one-sided Lagrange multiplier test is locally best invariant. An empirical power comparison suggests that if the block structure is known this test should be used. Otherwise the Durbin—Watson test provides a useful test, especially in large samples.

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线性回归误差协方差矩阵的局部最优不变检验AI 解读

SUMMARY This paper considers a class of hypothesis testing problems concerning the covariance matrix of the disturbances in the classical linear regression model. A test that is locally best invariant against one-sided alternative hypotheses is constructed and shown to be identical to a one-sided version of the Lagrange Multiplier test.

回归扰动移动平均相关的点最优检验AI 解读

This paper reconsiders King's [12] locally optimal test procedure for first-order moving average disturbances in the linear regression model. It recommends two tests, one for problems involving positively correlated disturbances and one for negatively correlated disturbances. Both tests are most powerful invariant at a point in the alternative hypothesis parameter space that is determined by a function involving the sample size and the number of regressors. Selected bounds…

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