HACcovariance {intrinsicFRP}

Heteroskedasticity and Autocorrelation robust covariance estimator

Description

This function estimates the long-run covariance matrix of a multivariate centred time series accounting for heteroskedasticity and autocorrelation using the Newey-West (1994) doi:10.2307/2297912 estimator. The number is selected using the Newey-West plug-in procedure, where n_lags = 4 * (n_observations/100)^(2/9). The function allows to internally prewhiten the series by fitting a VAR(1). All the details can be found in Newey-West (1994) doi:10.2307/2297912.

Usage

HACcovariance(series, prewhite = FALSE, check_arguments = TRUE)

Arguments

series

A matrix (or vector) of data where each column is a time series.

prewhite

A boolean indicating if the series needs prewhitening by fitting an AR(1). Default is FALSE

check_arguments

A boolean TRUE for internal check of all function arguments; FALSE otherwise. Default is TRUE.

Value

A symmetric matrix (or a scalar if only one column series is provided) representing the estimated HAC covariance.

Examples

# Import package data on 6 risk factors and 42 test asset excess returns
returns = intrinsicFRP::returns[,-1]
factors = intrinsicFRP::factors[,-1]

# Fit a linear model of returns on factors
fit = stats::lm(returns ~ factors)

# Extract residuals from the model
residuals = stats::residuals(fit)

# Compute the HAC covariance of the residuals
hac_covariance = HACcovariance(residuals)

# Compute the HAC covariance of the residuals imposing prewhitening
hac_covariance_pw = HACcovariance(residuals, prewhite = TRUE)