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检验看似不可检验的条件：识别性排除限制AI 解读

Method of moments estimation usually implies exploiting presumed uncorrelatedness of model disturbances and identifying instrumental variables. Here, utilizing non-orthogonality conditions is examined for linear multiple cross-section simultaneous regression models. Employing flexible bounds on the correlations between disturbances and regressors one avoids: (i) adoption of often incredible and unverifiable strictly zero correlation assumptions, and (ii) imprecise inference…

不同信息集与抽样方案下联立方程的识别和推断AI 解读

In simple static linear simultaneous equation models the empirical distributions of IV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. We demonstrate that the limiting distribution of consistent IV is not affected by conditioning on exogenous regressors, whereas that of inconsistent OLS is. The OLS asymptotic and simulated actual variances are shown to diminish by extending the set of exogenous variables…

动态反馈对面板模型 LS 与矩估计精度的影响AI 解读

The finite sample behaviour is analysed of particular least squares (LS) and a range of (generalized) method of moments (MM) estimators in panel data models with individual effects and both a lagged dependent variable regressor and another explanatory variable. The latter may be affected by lagged feedbacks from the de- pendent variable too. Asymptotic expansions indicate how the order of magnitude of bias of MM estimators tends to increase with the number of moment condi-…

含单位根动态回归模型中最小二乘估计量的矩近似AI 解读

To find approximations for bias, variance and mean-squared error of least-squares estimators for all coefficients in a linear dynamic regression model with a unit root, we derive asymptotic expansions and examine their accuracy by simulation. It is found that in this particular context useful expansions exist only when the autoregressive model contains at least one non-redundant exogenous explanatory variable. Surprisingly, the large-sample and small-disturbance asymptotic…

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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…

一阶自回归分布滞后模型的精确推断方法AI 解读

Exact tests and confidence sets are obtained for general transformations of the coefficients in linear first-order autoregressive models with exogenous variables and i.i.d. disturbances. The tests proposed have known level and are either similar (constant rejection probability under all processes consistent with the null hypothesis) or use bounds which are free of nuisance parameters. Correspondingly, the confidence sets are either similar with known size or conservative.…

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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…

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ARMA 误差对回归系数检验的影响：对 Vinod 文章的评论与扩展AI 解读

The problem that dependent disturbances may reverse the conclusions of t or F tests of the significance of the coefficients in least squares regression has been investigated by Vinod (1976). In this comment, imperfections in Vinod's article are indicated and improved results for a more general test are presented. Bounds on the critical values of the test of a set of linear constraints, including the simple significance test, are derived and calculated for disturbances…