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动态回归模型中扰动方差估计量的自由度调整AI 解读

In the classical regression model with fixed regressors the statistic S 2 , i.e. the sum of squared residuals (SSR) divided by the number of degrees of freedom, is an unbiased estimator of the variance of the disturbances. If the model is dynamic and contains lagged-dependent explanatory variables, then the least-squares coefficient estimators are biased in finite samples, and so is S 2 . By deriving the expectation of the initial terms in an expansion of the expression for…

含滞后因变量回归的不同偏差近似AI 解读

The small sample bias of the least-squares coefficient estimator is examined in the dynamic multiple linear regression model with normally distributed whitenoise disturbances and an arbitrary number of regressors which are all exogenous except for the one-period lagged-dependent variable. We employ large sample ( T → ∞) and small disturbance (σ → 0) asymptotic theory and derive and compare expressions to O ( T −1 ) and to O (σ 2 ), respectively, for the bias in the…