Profile least squares estimation of a partially linear time trend model with weakly dependent data

Zheng Li; Li Su; Daiqiang Zhang

doi:10.1016/j.econlet.2014.10.030

Profile least squares estimation of a partially linear time trend model with weakly dependent data

Zheng Li
, Li Su
, Daiqiang Zhang

Economics

University at Albany

Research output: Contribution to journal › Article › peer-review

Abstract

We consider a partially linear time trend model with weakly dependent data. We show that the semiparametric estimator for the time trend coefficient has the same rate of convergence as in the parametric time trend model case. We also show that the asymptotic variance reaches the semiparametric efficient bound. The Monte Carlo simulations strongly support our theoretical analysis.

Original language	English
Pages (from-to)	404-407
Number of pages	4
Journal	Economics Letters
Volume	125
Issue number	3
DOIs	https://doi.org/10.1016/j.econlet.2014.10.030
State	Published - Dec 1 2014

Keywords

Partially linear
Semiparametric bound, asymptotic normality
Time trend

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10.1016/j.econlet.2014.10.030

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title = "Profile least squares estimation of a partially linear time trend model with weakly dependent data",

abstract = "We consider a partially linear time trend model with weakly dependent data. We show that the semiparametric estimator for the time trend coefficient has the same rate of convergence as in the parametric time trend model case. We also show that the asymptotic variance reaches the semiparametric efficient bound. The Monte Carlo simulations strongly support our theoretical analysis.",

keywords = "Partially linear, Semiparametric bound, asymptotic normality, Time trend",

author = "Zheng Li and Li Su and Daiqiang Zhang",

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year = "2014",

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AU - Li, Zheng

AU - Su, Li

AU - Zhang, Daiqiang

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N2 - We consider a partially linear time trend model with weakly dependent data. We show that the semiparametric estimator for the time trend coefficient has the same rate of convergence as in the parametric time trend model case. We also show that the asymptotic variance reaches the semiparametric efficient bound. The Monte Carlo simulations strongly support our theoretical analysis.

AB - We consider a partially linear time trend model with weakly dependent data. We show that the semiparametric estimator for the time trend coefficient has the same rate of convergence as in the parametric time trend model case. We also show that the asymptotic variance reaches the semiparametric efficient bound. The Monte Carlo simulations strongly support our theoretical analysis.

KW - Partially linear

KW - Semiparametric bound, asymptotic normality

KW - Time trend

UR - https://www.scopus.com/pages/publications/84910006795

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DO - 10.1016/j.econlet.2014.10.030

M3 - Article

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VL - 125

SP - 404

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JO - Economics Letters

JF - Economics Letters

IS - 3

ER -

Profile least squares estimation of a partially linear time trend model with weakly dependent data

Abstract

Keywords

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