Convolutional autoregressive models for functional time series

Xialu Liu, Han Xiao, Rong Chen

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Functional data analysis has became an increasingly popular class of problems in statistical research. However, functional data observed over time with serial dependence remains a less studied area. Motivated by Bosq (2000), who first introduced the functional autoregressive models, we propose a convolutional functional autoregressive model, where the function at time t is a result of the sum of convolutions of the past functions and a set of convolution functions, plus a noise process, mimicking the vector autoregressive process. It provides an intuitive and direct interpretation of the dynamics of a stochastic process. Instead of principal component analysis commonly used in functional data analysis, we adopt a sieve estimation procedure based on B-spline approximation of the convolution functions. We establish convergence rate of the proposed estimator, and investigate its theoretical properties. The model building, model validation, and prediction procedures are also developed. Both simulated and real data examples are presented.

Original languageEnglish (US)
Pages (from-to)263-282
Number of pages20
JournalJournal of Econometrics
Volume194
Issue number2
DOIs
StatePublished - Oct 1 2016

ASJC Scopus subject areas

  • Economics and Econometrics

Keywords

  • Functional time series
  • Nonparametric methods
  • Sieve estimation
  • Spline methods

Fingerprint

Dive into the research topics of 'Convolutional autoregressive models for functional time series'. Together they form a unique fingerprint.

Cite this