Series estimation under cross-sectional dependence. / Lee, Jungyoon; Robinson, Peter M.

In: Journal of Econometrics, Vol. 190, No. 1, 01.2016, p. 1-17.

Research output: Contribution to journalArticlepeer-review

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Series estimation under cross-sectional dependence. / Lee, Jungyoon; Robinson, Peter M.

In: Journal of Econometrics, Vol. 190, No. 1, 01.2016, p. 1-17.

Research output: Contribution to journalArticlepeer-review

Harvard

Lee, J & Robinson, PM 2016, 'Series estimation under cross-sectional dependence', Journal of Econometrics, vol. 190, no. 1, pp. 1-17. https://doi.org/10.1016/j.jeconom.2015.08.001

APA

Lee, J., & Robinson, P. M. (2016). Series estimation under cross-sectional dependence. Journal of Econometrics, 190(1), 1-17. https://doi.org/10.1016/j.jeconom.2015.08.001

Vancouver

Author

Lee, Jungyoon ; Robinson, Peter M. / Series estimation under cross-sectional dependence. In: Journal of Econometrics. 2016 ; Vol. 190, No. 1. pp. 1-17.

BibTeX

@article{9b28edf1923242d3b3559a6e85f576f7,
title = "Series estimation under cross-sectional dependence",
abstract = "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.",
author = "Jungyoon Lee and Robinson, {Peter M}",
year = "2016",
month = jan,
doi = "10.1016/j.jeconom.2015.08.001",
language = "English",
volume = "190",
pages = "1--17",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Series estimation under cross-sectional dependence

AU - Lee, Jungyoon

AU - Robinson, Peter M

PY - 2016/1

Y1 - 2016/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.jeconom.2015.08.001

DO - 10.1016/j.jeconom.2015.08.001

M3 - Article

VL - 190

SP - 1

EP - 17

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 1

ER -