第7讲面板数据分解.ppt

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 24-* Example: Production Functions The estimated coefficients of 0.30 for capital and 0.69 for labor are similar to estimates using US data. We also get similar results using fixed effects estimation. TABLE 16.2 Fixed Effects Estimation of a Cobb–Douglas Production Function for a Sample of Manufacturing Firms 24-* 24-* Example: Production Functions We arrive at similar estimates using either random effects or fixed effects. Because only fixed effects controls for unobserved heterogeneity that is correlated with the explanators, the similarity between the two estimates suggests that unobserved heterogeneity is not creating a large bias in this sample. 24-* Example: Production Functions (cont.) The fixed effects estimator discards all variation between firms, and must use 624 more degrees of freedom than random effects. Moving from RE to FE increases the e.s.e. on capital from 0.0116 to 0.0145 The e.s.e. on labor moves from 0.0118 to 0.0132 24-* Example: Production Functions (cont.) The RE estimator provides more precise estimates We would prefer to use RE instead of FE. However, RE might be inconsistent if We need a test to help determine whether it is safe to use RE. 固定效应模型与随机效应模型 固定效应模型 优点 不需要事先假定固定效应与X无关； 估计系数具有一致性； 固定效应可能含有有用的信息。 缺点 自由度损失大，不适合用于观察对象众多的情况。 随机效应模型 优点 自由度损失小，适用于处理观察对象多的情况。 缺点 固定效应与X无关的假定不一定成立，因而有可能出现遗漏重要解释变量错误，此时无法保证估计系数的无偏性和一致性。 * 24-* The Hausman Test Hausman’s specification test for error components DGPs provides guidance on whether The key idea: if , then the inconsistent RE estimator and the consistent FE estimator converge to different estimates. 24-* The Hausman Test (cont.) If , then the unobserved heterogeneity is uncorrelated with X and does not create a bias. RE and FE are both consistent. For two consistent estimators to provide significantly different estimates would be surprising. 24-* The Hausman Test (cont.) We know the FE estimator