kernels: Kernel Functions for Long-Run Variance Estimation

In rbfmvar: Residual-Based Fully Modified Vector Autoregression

Kernel Functions for Long-Run Variance Estimation

Description

Kernel weight functions for heteroskedasticity and autocorrelation consistent (HAC) long-run variance estimation.

Details

These kernel functions are used in the estimation of long-run variance matrices following Andrews (1991) <doi:10.2307/2938229>. The kernels satisfy the conditions for consistent LRV estimation under weak dependence.

References

Andrews, D. W. K. (1991). Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59(3), 817-858. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2938229")}

Newey, W. K., & West, K. D. (1987). A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55(3), 703-708. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1913610")}

rbfmvar documentation built on April 9, 2026, 9:08 a.m.