Finite Sample Analysis of Misspecification in Simultaneous Equation Models
研究了联立方程模型中单个方程的设定错误对k类估计量精确抽样分布的影响，发现当设定错误可能性大时，普通最小二乘法通常优于两阶段最小二乘法。
Abstract Abstract This article examines the effects of misspecification on the exact sampling distributions of the k-class estimators of a single equation in a simultaneous equations model. The analysis focuses on the effects of excluding relevant exogenous variables. The misspecification may occur in either the estimated equation itself or in the other equations in the system. Exact expressions and large-concentration parameter asymptotic expansions are stated and analyzed for the bias and mean squared error (MSE) of the k-class estimators in the case of two included endogenous variables. The results in the article suggest that ordinary least squares (OLS) will often be preferable to two-stage least squares (2SLS) when misspecification is a serious possibility; the relative insensitivity of OLS to specification error outweighs its disadvantage in terms of bias and MSE in the correctly specified case. Further, when relevant exogenous variables are omitted from the estimated equation but not from the system, the entire k class, for nonstochastic k (0 ≤ k ≤ 1), is dominated in terms of asymptotic MSE by either OLS or 2SLS. Key Words: Finite sample analysisMisspecification k-class estimatorsSimultaneous equation models