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Sieve Extremum Estimates for Weakly Dependent Data
Abstract
Many non/semi-parametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary β-mixing observations, we obtain convergence rates of sieve extremum estimates and root-n asymptotic normality of plug-in sieve extremum estimates of smooth functionals. As applications to time series models, we give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, and wavelets; root-n asymptotic normality for partial linear additive AR(p) models, and monotone transformation AR(1) models.
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Cited by
Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
Ai, C., & Chen, X. (2003) · Econometrica · doi:10.1111/1468-0262.00470 · Cited by 726
Self-citationEstimation of semiparametric models when the criterion function is not smooth
Chen, X., Linton, O., & Van Keilegom, I. (2003) · Econometrica · doi:10.1111/1468-0262.00461 · Cited by 411
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Metadata
- Authors
- Chen, X., & Shen, X.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1998-03
- Volume
- 66
- Issue
- 2
- Pages
- 289
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2002287154
- Cited by
- 274
- References
- 0
- FWCI
- 3.94
- Type
- journal article
- Language
- es
- Open access
- closed
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- Chen, X., & Shen, X. (1998). Sieve Extremum Estimates for Weakly Dependent Data. Econometrica. https://doi.org/10.2307/2998559
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