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Haavelmo 对联立方程估计的贡献AI 解读

This paper surveys Trygve Haavelmo’s contributions to econometrics. A brief summary of his 1944 monograph is followed by an analysis of the six important papers he contributed during 1943–47. Four of them were devoted to macroeconomic models including estimation of the Keynesian marginal propensity to consume; the first two introduced the methodology of a system of structural equations; the third marked a milestone in econometric method by deriving the reduced form of such a…

残差自相关时线性趋势最小二乘估计的效率AI 解读

first-order stationary Markoff process with zero mean and autocorrelation coefficient p, - 1 < p < 1, the greatest lower bound for the efficiency of the least-squares estimator of 8 (relative to the Gauss-Markoff estimator) over the interval 0;p < 1 is .753763. This compares with a greatest lower bound of .535898 for the relative efficiency of the Cochrane-Orcutt estimator of 38.

一阶平稳马尔可夫残差下样本均值的效率AI 解读

The efficiency of the sample mean relative to the “best” linear unbiased estimator when residuals follow a first-order stationary Markoff process is studied. The minimum efficiency for correlation ρ ≧ 0 occurs at the unique root of a certain equation depending on the sample size n and decreases monotonically in n toward the value .878 obtained by Ylvisaker in the continuous case. It is tabulated for selected n ≦ 300, together with the value of p at which the minimum is…

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观测不足时的最小二乘法AI 解读

The optimality of the method of least squares is reconsidered when multicollinearity is present. An analysis is presented of the relationship between estimability and identifiability. The criterion of best linear minimum bias is developed, and shown to be equivalent to that of best linear conditionally unbiased estimation subject to complementary (non-estimable) linear restrictions. Imposition of erroneous estimable linear restrictions is shown to lower variances of…

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