Abstract: This paper considers semiparametric estimation of a nonstationary transformation model with panel data. While nonstationarity is a common phenomenon in applied research, one of the drawbacks of most existing semiparametric procedures is the requirement of stationarity assumption. In this paper, a new semiparametric estimator is proposed under a symmetry condition, allowing for nonstationarity of the error term. Under some mild regularity conditions, the proposed estimator is consistent and asymptotically normal. A simulation study illustrates its usefulness.
* This research was supported partly by research grants of SHUFE through 211-3-70. I would like to acknowledge useful discussion with professor Songnian Chen as well as seminar participants at HKUST and SHUFE for their valuable comments.