LagEstimator-class function

Class for a lag-window type estimator.

For a given time series Y a lag-window estimator of the Form [REMOVE_ME]$\hat{f}(\omega) = \sum_{|k|< n-1 } K_n(k) \Gamma(Y_0,Y_k) \exp(-i \omega k) [REMOVE_ME_2]$

will be calculated on initalization. The LagKernelWeight K_n is determined by the slot weight and the LagOperator $\Gamma(Y_0,Y_k)$ is defined by the slot lagOp. class

Description

For a given time series Y a lag-window estimator of the Form

will be calculated on initalization. The LagKernelWeight K_n is determined by the slot weight and the LagOperator $\Gamma(Y_0,Y_k)$ is defined by the slot lagOp.

Details

Currently, the implementation of this class allows only for the analysis of univariate time series.

Slots

• Y: the time series where the lag estimator was applied one

• weight: a Weight object to be used as lag window

• lagOp: a LagOperator object that determines which kind of bivariate structure should be calculated.

• env: An environment to allow for slots which need to be accessable in a call-by-reference manner:

   - **`sdNaive`**: An array used for storage of the naively estimated standard deviations of the smoothed periodogram.
   - **`sdNaive.done`**: a flag indicating whether `sdNaive`
          
          has been set yet.