Nonparametric-regression-based testing for omitted variables

基于非参数回归提出了同时适用于横截面和时间序列数据的遗漏变量检验统计量.与现有文献相比,该统计量不仅避免了模型设定偏误问题,而且具有更高的局部检验功效,能够识别出速度更快的收敛到原假设的局部备择假设.该文选择单一带宽估计条件联合期望和条件边际期望,允许二者的非参数估计误差共同决定统计量的渐近分布,不仅改善了统计量的有限样本性质,而且避免了选择多个带宽和计算多个偏差项产生的繁杂工作.蒙特卡洛模拟结果表明该统计量具有良好的有限样本性质以及比Ait-Sahalia等更高的检验功效.实证分析采用该统计量捕获了F统计量无法识别的产出缺口与通胀之间关系,验证了非线性"产出—通胀"型菲利普斯曲线在中国的适用性.This paper proposes a nonparametric-regression-based test for omitted variables,which is applicable in both cross-sectional and time series contexts. Our test not only avoids the model misspecification problem,but also are locally more powerful than the existing tests. Moreover,unlike many other nonparametric-based tests,we use a single bandwidth rather than two different bandwidths in estimating both the conditional joint and marginal expectations,which significantly improves the size performance of our test in finite samples.Monte Carlo studies demonstrate the well behavior of our test in finite samples,which could not only capture the omitted variables feature in linear and nonlinear regressions,but also is more powerful than Ait-Sahalia et al. 's( 2001) test. In an application to testing the nonlinear Granger causality in mean,we document the existence of nonlinear relationships between theoutput gap and inflation,that is,the nonlinear "output-inflation"type of Phillips curve maybe is more suitable for China's inflation forecast.国家自然科学青年基金资助项目(71401160);; 教育部人文社会科学研究青年基金资助项目(14YJC790120);; 中国科学院大学校部教师与研究所科研合作专项基金资助项