Indirect inference for fractional time series models
In this paper we present an indirect estimation procedure for (ARFIMA) fractional time series models. The estimation method is based on an 'incorrect' criterion which does not directly provide a consistent estimator of the parameters of interest, but leads to correct inference by using simulations. The main steps are the following. First, we consider an auxiliary model which can be easily estimated. Specifically, we choose the finite lag Autoregressive model. Then, this is estimated on the observations and simulated values drawn from the ARFIMA model associated with a given value of the parameters of interest. Finally, the latter is calibrated in order to obtain close values of the two estimators of the auxiliary parameters. In this article, we describe the estimation procedure and compare the performance of the indirect estimator with some alternative estimators based on the likelihood function by a Monte Carlo study.
indirect inference arfima model long memory time series