CovarianceMatrices.BartlettKernelType

Bartlett

Constructors

Bartlett(x::Int) Bartlett(::Type{Andrews}) Bartlett(::Type{NeweyWest})

Note

• Andrews: bandwidth selection a la Andrews
• NeweyWest: bandwidth selection a la Andrews
CovarianceMatrices.ParzenKernelType

Parzen

Constructors

Parzen(x::Int) Parzen(::Type{Andrews}) Parzen(::Type{NeweyWest})

Note

• Andrews: bandwidth selection a la Andrews
• NeweyWest: bandwidth selection a la Andrews
CovarianceMatrices.QuadraticSpectralKernelType

QuadraticSpectral

Constructors

Note

• Andrews: bandwidth selection a la Andrews
• NeweyWest: bandwidth selection a la Andrews
CovarianceMatrices.TruncatedKernelType

TruncatedKernel

Constructors

Truncated(x::Int) Truncated{Andrews}() Truncated{NeweyWest}()

Note

• Fixed: fixed bandwidth
• Andrews: bandwidth selection a la Andrews
• NeweyWest: bandwidth selection a la Andrews
CovarianceMatrices.TukeyHanningKernelType

TukeyHanning

Constructors

TukeyHanning(x::Int) TukeyHanning(::Type{Andrews}) TukeyHanning(::Type{NeweyWest})

Note

• Andrews: bandwidth selection a la Andrews
• NeweyWest: bandwidth selection a la Andrews
CovarianceMatrices.aVarMethod

Asymptotic Variance Estimators

aVar(k::AVarEstimator, m::AbstractMatrix{T}; demean::Bool=true, dims::Int=1, means::Union{Nothing, AbstractArray}=nothing, prewhite::Bool=false, scale=true)

Caclulate the asymptotic variance of \bar{X}{,j}=\frac{1}{bn}\sum{i=1}^n X{i,j}, where \bar{X}{,j} is the j-th column of X.

Arguments

- k::AVarEstimator
- demean=false: whether the data should be demeaned.
- prewhite=false: should the data be prewithened. Relevant for HAC estimator.
CovarianceMatrices.optimalbwMethod

optimalbandwidth(k::HAC{T}, mm; prewhite::Bool=false) where {T<:Andrews} optimalbandwidth(k::HAC{T}, mm; prewhite::Bool=false) where {T<:NeweyWest}

Calculate the optimal bandwidth according to either Andrews or Newey-West.