Pooling Under Misspecification: Some Monte Carlo Evidence on the Kmenta and the Error Components Techniques
误设下的合并估计：Kmenta 与误差分量方法的蒙特卡洛证据AI 解读 ↓
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误设下的合并估计：Kmenta 与误差分量方法的蒙特卡洛证据
- 研究问题
- 合并截面与时间序列数据时，若误把误差分量结构当作异方差和序列相关结构，或反向误判，会造成怎样的估计损失？
- 方法与思路
- 作者在两类真实误差结构下比较 Kmenta 方法与误差分量方法，以蒙特卡洛实验评估使用错误估计方法的损失。
- 材料陈述的贡献
- 摘要称，论文比较两种合并技术的长短处和误设损失，并建议利用同方差及序列相关检验区分两类误差结构。
- 阅读关注点
- 应核对模拟中的误差结构、样本维度与损失指标，以及 Bartlett 和广义 Durbin–Watson 检验能否可靠指导方法选择。
- 阅读边界
- 解读限于所给 Crossref 摘要。摘要未提供模拟参数、损失数值或检验表现，两个简单设定不足以确立普遍优劣。
原文摘要
Two different methods for pooling time series of cross section data are used by economists. The first method, described by Kmenta, is based on the idea that pooled time series of cross sections are plagued with both heteroskedasticity and serial correlation.The second method, made popular by Balestra and Nerlove, is based on the error components procedure where the disturbance term is decomposed into a cross-section effect, a time-period effect, and a remainder.Although these two techniques can be easily implemented, they differ in the assumptions imposed on the disturbances and lead to different estimators of the regression coefficients. Not knowing what the true data generating process is, this article compares the performance of these two pooling techniques under two simple setting. The first is when the true disturbances have an error components structure and the second is where they are heteroskedastic and time-wise autocorrelated. First, the strengths and weaknesses of the two techniques are discussed. Next, the loss from applying the wrong estimator is evaluated by means of Monte Carlo experiments. Finally, a Bartletfs test for homoskedasticity and the generalized Durbin-Watson test for serial correlation are recommended for distinguishing between the two error structures underlying the two pooling techniques.