Inference on Moment Inequalities with Unknown Functions
2014/03/21
研討會日期 : 2014-03-21
時間 : 14:30
主講人 : Professor Shengjie Hong
地點 : Conference Room B110
演講者簡介 : Professor Hong received his Ph.D. in Economics from University of Wisconsin-Madison in 2012. He is currently an Assistant Professor at Department of Economics in Tsinghua University. His research fields are Econometric Theory and Applied Econometrics.
演講摘要 : In this paper, we consider inference on conditional moment inequalities, in which the unknown parameter may contain infinite-dimensional components. We transfer moment inequalities into equalities by introducing a slackness function as a nuisance parameter. For inference on these equalities, we propose using the sieve minimum of a Kolmogorov-Smirnov type statistic as the test statistic, derive its asymptotic distribution, and provide consistent bootstrap critical values. Our methods are robust to partial identification, and allow for the moment functions to be nonsmooth. We extend Hong’s (2012) analysis on conditional moment equalities in the following two directions: First, by allowing for moment inequality constraints, our inference method has a lot more applications; Second, the consistency of our test holds uniformly over a broad family of data generating processes.
