We propose the use of indirect inference estimation for inference in 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 the model under study , the resulting estimators display nonparametric rates of convergence and behavior . We validate our methodology via simulation studies in the confines of a locally stationary moving average model and a  locally stationary multiplicative stochastic volatility model. An application of the methodology gives evidence of non-linear, time-varying volatility  for monthly returns on the Fama-French portfolios.
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.
R
for inference in
to conduct inference in complex
R
the model under study
locally stationary models
R
estimators display
indirect inference estimator exhibits
D
and behavior
R
via
with
A
new
R
An application of the methodology gives
Using this indirect inference methodology and the new locally stationary volatility model, we obtain
A
trends
R
the
several