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自回归误差回归模型预测的小样本性质AI 解读

Monte Carlo methods are used to examine the small-sample properties of various estimators of asymptotic prediction variance (APV) from the regression model with autoregressive errors. Two practical estimators of APV, one of which includes terms reflecting parameter estimation and one of which excludes these terms, are compared to the mean squared error of prediction for regression models with different autocorrelation of the errors, different sample sizes, and different…

自回归误差回归模型预测的小样本性质AI 解读

Monte Carlo methods are used to examine the small-sample properties of various estimators of asymptotic prediction variance (APV) from the regression model with autoregressive errors. Two practical estimators of APV, one of which includes terms reflecting parameter estimation and one of which excludes these terms, are compared to the mean squared error of prediction for regression models with different autocorrelation of the errors, different sample sizes, and different…

含Box–Cox变换模型参数估计的快速高效算法AI 解读

A modified Newton algorithm for the estimation of parameters in models containing the Box-Cox transformation is presented. It is shown that the usual maximum likelihood estimator for the k linear parameters and the m power transformation parameters may be specified as an m-parameter nonlinear least squares estimator. Several models containing Box-Cox transformations are estimated and the speed and efficiency of the modified algorithm compared with three other gradient…

自相关误差下非线性最小二乘与最大似然估计的小样本性质AI 解读

Rao and Griliches (1969) compared several methods of parameter estimation in models having autocorrelated errors. They concluded that nonlinear least squares estimators were not superior to two-stage linear estimators. This study partially replicates the Rao and Griliches Monte Carlo simulation and, in addition, examines the maximum likelihood estimator as a possible competitor. The simulation results are not consistent with those of Rao and Griliches; the small-sample…

Box–Cox 变换小样本性质的蒙特卡洛研究AI 解读

The small-sample properties of models which transform both the dependent and independent variables using the same Box-Cox transformation are investigated in sampling experiments. Bias does not appear to be a serious problem; however, the sign and size of the transformation parameter, which changes the coefficient of variation of the dependent variable, seriously affects the variances of the estimators. Hypothesis tests reveal that t statistics frequently lead to incorrect…

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