Series estimation under cross-sectional dependence
By Jungyoon Lee, Peter M. Robinson. 2013
53 citations recorded by the source corpus.
An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, including conditional and unconditional heteroscedasticity, along with conditions on regressors that allow dependence and do not require existence of a density. The conditions aim to accommodate various settings plausible in economic applications, and can apply also to panel, spatial and time series data. A mean square rate of convergence of nonparametric regression estimates is established followed by asymptotic normality of a quite general statistic. Data-driven studentizations that rely on single or double indices to order the data are justified. In a partially linear model setting, Monte Carlo investigation of finite sample properties and two empirical applications are carried out.
Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/88b19c9322eb696283d29fea3d9516e06b16dc9e
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Citations in the corpus
- A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks (2017, methodology): “When establishing asymptotic properties for our model and estimator below, we follow Li et al. (2013) and distinguish between the case where β(z) is not a constant function with respect to zs for s = 1, 2, .”