Identification and estimation of nonseparable single-index models in panel data with correlated random effects
研究了面板数据中具有相关随机效应的不可分离单指数模型的识别问题，提出基于局部多项式平滑的平均导数差估计量，并推导了方差最小化加权方案，适用于固定时间期数。
The identification in a nonseparable single-index models with correlated random effects is considered in panel data with a fixed number of time periods. The identification assumption is based on the correlated random effects structure. Under this assumption, the parameters of interest are identified up to a multiplicative constant and could be estimated by an average difference of derivatives estimator based on the local polynomial smoothing. We suggest to linearly combine the estimators obtained for different orders of differences and derive the variance-minimizing weighting scheme. The asymptotic distribution of the proposed estimators is derived both for stationary and non-stationary explanatory variables along with a test of the stationarity. Finally, Monte Carlo experiments reveal finite sample properties of the proposed estimator.