Monte Carlo Methodology and the Finite Sample Properties of Instrumental Variables Statistics for Testing Nested and Non-Nested Hypotheses
用蒙特卡洛方法研究动态性和同时性对检验嵌套和非嵌套假设的工具变量统计量有限样本性质的影响，得到近似未知功效函数的简单公式，并指出F形式和Wald统计量优于卡方形式，大sigma和小有效样本量严重影响过度识别检验和Cox型检验。
Using Monte Carlo methodology, this paper investigates the effect of dynamics and simultaneity on the finite sample properties of instrumental variables statistics for testing nested and nonnested hypotheses. Simple numerical-analytical formulae (response surfaces) are obtained which closely approximate the statistics' unknown size and power functions for a dynamic simultaneous-equations model. The analysis illustrates the value and limitations of asymptotic theory in interpreting finite sample properties. Two practical results arise. The F form and the Wald statistic is favored over its chi-squared form, and large-sigma and small effective sample size strongly affect the test of over-identifying restrictions and the Cox-type test. Copyright 1991 by The Econometric Society.