Fast inference for semi-varying coefficient models via local averaging

Heng PENG, Chuanlong Xie*, Jingxin Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The semi-varying coefficient models are widely used in the application of finance, economics, medical science and many other areas. In general, the functional coefficients are estimated by local smoothing methods, e.g. local linear estimator. So the computation cost is severe because one should point-wisely estimate the value of a coefficient function. In this paper, we give an insight into the trade-off between statistical efficiency and computation simplicity and proposes a fast inference procedure, local average estimator. The proposed method is easy to implement and avoid repeat estimation since it approximates the coefficient functions with piecewise constants. Though the local average estimator is not asymptotically optimal, it is still efficient enough for further inference. Thus, three tests are derived to check whether a coefficient is constant. The experimental evidence shows that when there is limited room for improving the asymptotic efficiency, a proper trade-off between statistical efficiency and computation simplicity may improve the finite-sample performance.

Original languageEnglish
Article number107126
JournalComputational Statistics and Data Analysis
Volume157
DOIs
Publication statusPublished - May 2021

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Asymptotic efficiency
  • Computation cost
  • Hypothesis test
  • Local average estimate
  • Varying coefficient

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