Indirect inference for locally stationary models

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Abstract

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving average model and a new locally stationary multiplicative stochastic volatility model. Using this indirect inference methodology and the new locally stationary volatility model, we obtain evidence of non-linear, time-varying volatility trends for monthly returns on several Fama–French portfolios.

Original languageEnglish
Pages (from-to)1-27
Number of pages27
JournalJournal of Econometrics
Volume223
Issue number1
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Indirect inference
  • Locally stationary
  • Semiparametric
  • State–space models

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