CovarianceMatrices.jl

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation for Julia.

Installation

Pkg.add("CovarianceMatrices")

Introduction

This package provides types and methods useful to obtain consistent estimates of the long run covariance matrix of a random process.

Three classes of estimators are considered:

HAC - heteroskedasticity and autocorrelation consistent (Andrews, 1996; Newey and West, 1994)
VARHAC - Vector Autoregression based HAC (Den Haan and Levine)
Smoothed - (Smith, 2014)
HC - hetheroskedasticity consistent (White, 1982)
CRVE - cluster robust (Arellano, 1986)

The typical application of these estimators is to conduct robust inference about parameters of a statistical model.

The package extends methods defined in StatsBase.jl and GLM.jl to provide a plug-and-play replacement for the standard errors calculated by default by GLM.jl.

The API can be used regardless of whether the model is fit with GLM.jl and developer can extend their fit functions to provides robust standard errors.

Quick tour

HAC (Heteroskedasticity and Autocorrelation Consistent)

Available kernel types are:

TruncatedKernel
BartlettKernel
ParzenKernel
TukeyHanningKernel
QuadraticSpectralKernel

For example, ParzenKernel{NeweyWest}() return an instance of TruncatedKernel parametrized by NeweyWest, the type that corresponds to the optimal bandwidth calculated following Newey and West (1994). Similarly, ParzenKernel{Andrews}() corresponds to the optimal bandwidth obtained in Andrews (1991). If the bandwidth is known, it can be directly passed, i.e. TruncatedKernel(2).

Long run variance of regression coefficients

In the regression context, the function vcov does all the work:

vcov(::HAC, ::DataFrameRegressionModel; prewhite = true)

Consider the following artificial data (a regression with autoregressive error component):

using CovarianceMatrices
using Random, DataFrames, GLM
Random.seed!(1)
n = 500
x = randn(n,5)
u = zeros(2*n)
u[1] = rand()
for j in 2:2*n
    u[j] = 0.78*u[j-1] + randn()
end
u = u[n+1:2*n]
y = 0.1 .+ x*[0.2, 0.3, 0.0, 0.0, 0.5] + u

df = convert(DataFrame,x)
df[!,:y] = y

Using the data in df, the coefficient of the regression can be estimated using GLM

lm1 = glm(@formula(y~x1+x2+x3+x4+x5), df, Normal(), IdentityLink())

To get a consistent estimate of the long run variance of the estimated coefficients using a Quadratic Spectral kernel with automatic bandwidth selection à la Andrews

vcov(QuadraticSpectralKernel{Andrews}(), lm1, prewhite = false)

If one wants to estimate the long-time variance using the same kernel, but with a bandwidth selected à la Newey-West

vcov(QuadraticSpectralKernel{NeweyWest}(), lm1, prewhite = false)

The standard errors can be obtained by the stderror method

stderror( ::HAC, ::DataFrameRegressionModel; prewhite::Bool)

For the previous example:

stderror(QuadraticSpectralKernel{NeweyWest}(), lm1, prewhite = false)

Sometime is useful to access the bandwidth selected by the automatic procedures. This can be done using the optimalbandwidth method

optimalbandwidth(QuadraticSpectralKernel{NeweyWest}(), lm1; prewhite = false)
optimalbandwidth(QuadraticSpectralKernel{Andrews}(), lm1; prewhite = false)

Alternatively, the optimal bandwidth is stored in the kernel structure (upon calculation of the variance) and can be accessed. This requires however that the kernel type is materialized:

kernel = QuadraticSpectralKernel{NeweyWest}()
stderror(kernel, lm1, prewhite = false)
bw = CovarianceMatrices.bandwidth(kernel)

Covariances without `GLM.jl`

One might want to calculate variance estimator when the regression (or some other model) is fit "manually". Below is an example of how this can be accomplished.

X   = [ones(n) x]
_,K = size(X)
b   = X\y
res = y .- X*b
momentmatrix = X.*res
B   = inv(X'X)      # Jacobian of moment conditions
bw = CovarianceMatrices.optimalbandwidth(kernel, momentmatrix, prewhite=false)
A   = lrvar(QuadraticSpectralKernel(bw), momentmatrix, scale = n^2/(n-K))   # df adjustment is built into vcov
Σ   = B*A*B
Σ .- vcov(QuadraticSpectralKernel(bw), lm1, dof_adjustment=true)

The utility function sandwich does all this automatically:

vcov(QuadraticSpectralKernel(bw[1]), lm1, dof_adjustment=true) ≈ CovarianceMatrices.sandwich(QuadraticSpectralKernel(bw[1]), B, momentmatrix, dof = K)
vcov(QuadraticSpectralKernel(bw[1]), lm1, dof_adjustment=false) ≈ CovarianceMatrices.sandwich(QuadraticSpectralKernel(bw[1]), B, momentmatrix, dof = 0)

HC (Heteroskedastic consistent)

As in the HAC case, vcov and stderror are the main functions. They know get as argument the type of robust variance being sought

vcov(::HC, ::DataFrameRegressionModel)

Where HC is an abstract type with the following concrete types:

HC0
HC1 (this is HC0 with the degree of freedom adjustment)
HC2
HC3
HC4
HC4m
HC5

using CovarianceMatrices, DataFrames, GLM
# A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
# The weights are added just to test the interface and are not part
# of the original data
clotting = DataFrame(
    u    = log.([5,10,15,20,30,40,60,80,100]),
    lot1 = [118,58,42,35,27,25,21,19,18],
    lot2 = [69,35,26,21,18,16,13,12,12],
    w    = 9*[1/8, 1/9, 1/25, 1/6, 1/14, 1/25, 1/15, 1/13, 0.3022039]
)
wOLS = fit(GeneralizedLinearModel, @formula(lot1~u), clotting, Normal(), wts = clotting[!,:w])

vcov(HC0(),wOLS)
vcov(HC1(),wOLS)
vcov(HC2(),wOLS)
vcov(HC3(),wOLS)
vcov(HC4(),wOLS)
vcov(HC4m(),wOLS)
vcov(HC5(),wOLS)

CRHC (Cluster robust heteroskedasticity consistent)

The API of this class of estimators is subject to change, so please use with care. The difficulty is that CRHC type needs to have access to the variable along which dimension the clustering must take place. For the moment, the following approach works

using RDatasets
df = dataset("plm", "Grunfeld")
lm = glm(@formula(Inv~Value+Capital), df, Normal(), IdentityLink())
vcov(CRHC1(:Firm, df), lm)
stderror(CRHC1(:Firm, df),lm)

Alternatively, the cluster indicator can be passed directly (but this will only work if there are not missing values)

vcov(CRHC1(df[:Firm]), lm)
stderror(CRHC1(df[:Firm]),lm)

As in the HAC case, sandwich and lrvar can be leveraged to constract cluster-robust variances without relying on GLM.jl.

Performances

using BenchmarkTools
## Calculating a HAC on a large matrix
Z = randn(10000, 10)
@btime lrvar(BartlettKernel{Andrews}(), Z, prewhite = true) 
## 2.085 ms (180 allocations: 6.20 MiB)

library(sandwich)
library(microbenchmark)
Z <- matrix(rnorm(10000*10), 10000, 10)
microbenchmark( "Bartlett/Newey" = {lrvar(Z, type = "Andrews", kernel = "Bartlett")})
#Unit: milliseconds
#           expr      min       lq     mean   median       uq      max     neval
# Bartlett/Andrews 135.1839 148.3426 186.1966 155.0156 246.3178 355.3174   100