Nonparametric spatial regression under near-epoch dependence
This paper establishes asymptotic normality and uniform consistency with convergence rates of the local linear estimator for spatial near-epoch dependent (NED) processes. The class of the NED spatial processes covers important spatial processes, including nonlinear autoregressive and infinite moving...
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| Veröffentlicht in: | Journal of econometrics 2012-03, Vol.167 (1), p.224-239 |
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| container_title | Journal of econometrics |
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| creator | Jenish, Nazgul |
| description | This paper establishes asymptotic normality and uniform consistency with convergence rates of the local linear estimator for spatial near-epoch dependent (NED) processes. The class of the NED spatial processes covers important spatial processes, including nonlinear autoregressive and infinite moving average random fields, which generally do not satisfy mixing conditions. Apart from accommodating a larger class of dependent processes, the proposed asymptotic theory allows for triangular arrays of heterogeneous random fields located on unevenly spaced lattices and sampled over regions of arbitrary configuration. All these features make the results applicable in a wide range of empirical settings. |
| doi_str_mv | 10.1016/j.jeconom.2011.11.008 |
| format | Article |
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| ispartof | Journal of econometrics, 2012-03, Vol.167 (1), p.224-239 |
| issn | 0304-4076 1872-6895 |
| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Asymptotic methods Asymptotic normality Consistency Convergence Econometric models Econometrics Empirical research Estimating techniques Estimation Heterogeneous random fields Local linear estimator Near-epoch dependent processes Near-epoch dependent spatial processes Non-linear models Nonparametric regression Random variables Regression analysis Spatial analysis Studies |
| title | Nonparametric spatial regression under near-epoch dependence |
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