cov_matrix_estimator {slm}

Covariance matrix estimator for slm object

Description

This function gives the estimation of the asymptotic covariance matrix of the normalized least squares estimator in the case of the linear regression model with strictly stationary errors.

Usage

cov_matrix_estimator(object)

Arguments

object

an object of class slm.

Details

The function computes the covariance matrix estimator of the normalized least squares estimator from the vector cov_st of a slm object. If the user has given the argument Cov_ST in the slm object, then it is used to compute the final covariance matrix. If the method used is the "hac" method, then the final covariance matrix is computed via the kernHAC function of the sandwich package, by using the Quadratic Spectral kernel and the bandwidth described in Andrews (1991). For the methods "efromovich", "kernel" and "select", the covariance matrix estimator may not be positive definite. Then we apply the "Positive definite projection" algorithm, which consists in replacing all eigenvalues lower or equal to zero with the smallest positive eigenvalue of the covariance matrix.

Value

This function returns the estimation of the asymptotic covariance matrix of the normalized least squares estimator.

References

D. Andrews (1991). Heteroskedasticity and autocorrelation consistent covariant matrix estimation. Econometrica, 59(3), 817-858.

E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583. https://arxiv.org/abs/1906.06583.

A. Zeileis (2004). Econometric computing with HC and HAC covariance matrix estimators.

See Also

The R package sandwich.

kernHAC for HAC methods.