Compare ⋅ s-broda/ARCHModels.jl

    end
end

mutable struct UnivariateSubsetARCHModel{T<:AbstractFloat,
                 				   VS<:UnivariateVolatilitySpec,
                 		  	  	   SD<:StandardizedDistribution{T},
                 				   MS<:MeanSpec{T},
								   N
                 				   } <: ARCHModel
	spec::VS
    data::Vector{T}
    dist::SD
    meanspec::MS
	fitted::Bool
	subset::NTuple{N, Int}
    function UnivariateSubsetARCHModel{T, VS, SD, MS, N}(spec, data, dist, meanspec, fitted, subset) where {T, VS, SD, MS, N}
        new(spec, data, dist, meanspec, fitted, subset)
    end
end

"""
    UnivariateARCHModel(spec::UnivariateVolatilitySpec, data::Vector; dist=StdNormal(),
	          			meanspec=NoIntercept(), fitted=false

@@ -75,14 +92,38 @@

Loading

    UnivariateARCHModel{T, VS, SD, MS}(spec, data, dist, meanspec, fitted)
end

function UnivariateSubsetARCHModel(spec::VS,
          		 			 data::Vector{T};
          					 dist::SD=StdNormal{T}(),
          				 	 meanspec::MS=NoIntercept{T}(),
		  			 		 fitted::Bool=false,
							 subset::NTuple{N, Int}
							 ) where {T<:AbstractFloat,
                    			 	  VS<:UnivariateVolatilitySpec,
                   					  SD<:StandardizedDistribution,
                   					  MS<:MeanSpec,
									  N
                   			 		  }
    UnivariateSubsetARCHModel{T, VS, SD, MS, N}(spec, data, dist, meanspec, fitted, subset)
end
loglikelihood(am::UnivariateARCHModel) = loglik(typeof(am.spec), typeof(am.dist),
                                      am.meanspec, am.data,
                                      vcat(am.spec.coefs, am.dist.coefs,
                                           am.meanspec.coefs
                                           )
                                      )

loglikelihood(am::UnivariateSubsetARCHModel) = loglik(typeof(am.spec), typeof(am.dist),
                                      am.meanspec, am.data,
                                      vcat(am.spec.coefs, am.dist.coefs,
                                           am.meanspec.coefs
                                           ),
									  subsetmask(typeof(am.spec), am.subset)
									  )


dof(am::UnivariateARCHModel) = nparams(typeof(am.spec)) + nparams(typeof(am.dist)) + nparams(typeof(am.meanspec))
dof(am::UnivariateSubsetARCHModel) = nparams(typeof(am.spec), am.subset) + nparams(typeof(am.dist)) + nparams(typeof(am.meanspec))
coef(am::UnivariateARCHModel)=vcat(am.spec.coefs, am.dist.coefs, am.meanspec.coefs)
coefnames(am::UnivariateARCHModel) = vcat(coefnames(typeof(am.spec)),
                                coefnames(typeof(am.dist)),

@@ -232,19 +273,17 @@

Loading

#but grows them by length(data); hence it should be called with an empty one-
#dimensional array of the right type.
@inline function loglik!(ht::AbstractVector{T2}, lht::AbstractVector{T2},
                         zt::AbstractVector{T2}, at::AbstractVector{T2}, ::Type{VS}, ::Type{SD}, meanspec::MS,
                         data::Vector{T1}, coefs::AbstractVector{T3}
                         zt::AbstractVector{T2}, at::AbstractVector{T2}, vs::Type{VS}, ::Type{SD}, meanspec::MS,
                         data::Vector{T1}, coefs::AbstractVector{T3}, subsetmask=trues(nparams(vs))
                         ) where {VS<:UnivariateVolatilitySpec, SD<:StandardizedDistribution,
                                  MS<:MeanSpec, T1<:AbstractFloat, T2, T3
                                  }
    garchcoefs, distcoefs, meancoefs = splitcoefs(coefs, VS, SD, meanspec)
    #the below 6 lines can be removed when using Fminbox
    lowergarch, uppergarch = constraints(VS, T1)
    lowerdist, upperdist = constraints(SD, T1)
	lowergarch, uppergarch = constraints(VS, T1)
	lowerdist, upperdist = constraints(SD, T1)
    lowermean, uppermean = constraints(MS, T1)
    lower = vcat(lowergarch, lowerdist, lowermean)
    upper = vcat(uppergarch, upperdist, uppermean)
    all(lower.<coefs.<upper) || return T2(-Inf)
    all(lowerdist.<distcoefs.<upperdist) && all(lowermean.<meancoefs.<uppermean) && all(lowergarch[subsetmask].<garchcoefs[subsetmask].<uppergarch[subsetmask]) || return T2(-Inf)
	garchcoefs .*= subsetmask
    T = length(data)
	r1 = presample(VS)
	r2 = presample(meanspec)

@@ -280,7 +319,7 @@

Loading

end#function

function loglik(spec::Type{VS}, dist::Type{SD}, meanspec::MS,
                   data::Vector{<:AbstractFloat}, coefs::AbstractVector{T2}
                   data::Vector{<:AbstractFloat}, coefs::AbstractVector{T2}, subsetmask=trues(nparams(spec))
                   ) where {VS<:UnivariateVolatilitySpec, SD<:StandardizedDistribution,
                            MS<:MeanSpec, T2
                            }

@@ -290,7 +329,7 @@

Loading

    lht = CircularBuffer{T2}(r)
    zt = CircularBuffer{T2}(r)
	at = CircularBuffer{T2}(r)
    loglik!(ht, lht, zt, at, spec, dist, meanspec, data, coefs)
    loglik!(ht, lht, zt, at, spec, dist, meanspec, data, coefs, subsetmask)

end


@@ -325,13 +364,6 @@

Loading

                       }
    obj = x -> -loglik(VS, SD, meanspec, data, x)
    coefs = vcat(garchcoefs, distcoefs, meancoefs)
    #for fminbox:
    # lowergarch, uppergarch = constraints(VS, T)
    # lowerdist, upperdist = constraints(SD, T)
    # lowermean, uppermean = constraints(MS, T)
    # lower = vcat(lowergarch, lowerdist, lowermean)
    # upper = vcat(uppergarch, upperdist, uppermean)
    # res = optimize(obj, lower, upper, coefs, Fminbox(algorithm); autodiff=autodiff, kwargs...)
    res = optimize(obj, coefs, algorithm; autodiff=autodiff, kwargs...)
    coefs .= Optim.minimizer(res)
    ng = nparams(VS)

@@ -396,6 +428,34 @@

Loading

396	428		return UnivariateARCHModel(VS(coefs), data; dist=SD(distcoefs), meanspec=ms, fitted=true)
397	429		end
398	430
	431	+	function fitsubset(::Type{VS}, data::Vector{T}, maxlags::Int, subset::Tuple; dist::Type{SD}=StdNormal{T},
	432	+	meanspec::Union{MS, Type{MS}}=Intercept{T}(T[0]), algorithm=BFGS(),
	433	+	autodiff=:forward, kwargs...
	434	+	) where {VS<:UnivariateVolatilitySpec, SD<:StandardizedDistribution,
	435	+	MS<:MeanSpec, T<:AbstractFloat
	436	+	}
	437	+	#can't use dispatch for this b/c meanspec is a kwarg
	438	+	meanspec isa Type ? ms = meanspec(zeros(T, nparams(meanspec))) : ms = deepcopy(meanspec)
	439	+	VS_large = VS{ntuple(i->maxlags, length(subset))...}
	440	+	ng = nparams(VS_large)
	441	+	ns = nparams(SD)
	442	+	nm = nparams(typeof(ms))
	443	+	mask = subsetmask(VS_large, subset)
	444	+	garchcoefs = startingvals(VS_large, data, subset)
	445	+	distcoefs = startingvals(SD, data)
	446	+	meancoefs = startingvals(ms, data)
	447	+
	448	+	obj = x -> -loglik(VS_large, SD, ms, data, x, mask)
	449	+	coefs = vcat(garchcoefs, distcoefs, meancoefs)
	450	+	res = optimize(obj, coefs, algorithm; autodiff=autodiff, kwargs...)
	451	+	coefs .= Optim.minimizer(res)
	452	+	garchcoefs .= coefs[1:ng]
	453	+	distcoefs .= coefs[ng+1:ng+ns]
	454	+	meancoefs .= coefs[ng+ns+1:ng+ns+nm]
	455	+	ms.coefs .= meancoefs
	456	+	return UnivariateSubsetARCHModel(VS_large(garchcoefs), data; dist=SD(distcoefs), meanspec=ms, fitted=true, subset=subset)
	457	+	end
	458	+
399	459		function fit!(am::UnivariateARCHModel; algorithm=BFGS(), autodiff=:forward, kwargs...)
400	460		am.spec.coefs.=startingvals(typeof(am.spec), am.data)
401	461		am.dist.coefs.=startingvals(typeof(am.dist), am.data)

@@ -475,14 +535,15 @@

Loading

	mylock=Threads.ReentrantLock()
    ndims = max(my_unwrap_unionall(VS)-1, 0) # e.g., two (p and q) for GARCH{p, q, T}
	ndims2 = max(my_unwrap_unionall(MS)-1, 0 )# e.g., two (p and q) for ARMA{p, q, T}
    res = Array{UnivariateARCHModel, ndims+ndims2}(undef, ntuple(i->maxlags - minlags + 1, ndims+ndims2))
    res = Array{UnivariateSubsetARCHModel, ndims+ndims2}(undef, ntuple(i->maxlags - minlags + 1, ndims+ndims2))
    Threads.@threads for ind in collect(CartesianIndices(size(res)))
		VSi = VS{ind.I[1:ndims] .+ minlags .-1...}
		MSi = (ndims2==0 ? meanspec : meanspec{ind.I[ndims+1:end] .+ minlags .- 1...})
		res[ind] = fit(VSi, data; dist=dist, meanspec=MSi,
		tup = (ind.I[1:ndims] .+ minlags .-1)
		MSi = (ndims2==0 ? deepcopy(meanspec) : meanspec{ind.I[ndims+1:end] .+ minlags .- 1...})
		res[ind] = fitsubset(VS, data, maxlags, tup; dist=dist, meanspec=MSi,
                       algorithm=algorithm, autodiff=autodiff, kwargs...)
        if show_trace
            lock(mylock)
			VSi = VS{tup...}
            Core.print(modname(VSi))
			ndims2>0 && Core.print("-", modname(MSi))
			Core.println(" model has ",

@@ -494,10 +555,9 @@

Loading

    end
    crits = criterion.(res)
    _, ind = findmin(crits)
    return res[ind]
	return fit(VS{res[ind].subset...}, data; dist=dist, meanspec=res[ind].meanspec, algorithm=algorithm, autodiff=autodiff, kwargs...)
end


function coeftable(am::UnivariateARCHModel)
    cc = coef(am)
    se = stderror(am)

@@ -71,9 +71,11 @@

Loading

const ARCH = GARCH{0}

@inline nparams(::Type{<:TGARCH{o, p, q}}) where {o, p, q} = o+p+q+1
@inline nparams(::Type{<:TGARCH{o, p, q}}, subset) where {o, p, q} = isempty(subset) ? 1 : sum(subset) + 1

@inline presample(::Type{<:TGARCH{o, p, q}}) where {o, p, q} = max(o, p, q)


Base.@propagate_inbounds @inline function update!(
        ht, lht, zt, at, ::Type{<:TGARCH{o, p, q}}, garchcoefs
        ) where {o, p, q}

@@ -112,6 +114,19 @@

Loading

    return x0
end

function startingvals(TT::Type{<:TGARCH}, data::Array{T} , subset::Tuple) where {T}
	o, p, q = subsettuple(TT, subsetmask(TT, subset)) # defend against (p, q) instead of (o, p, q)
	x0 = zeros(T, o+p+q+1)
    x0[2:o+1] .= 0.04/o
    x0[o+2:o+p+1] .= 0.9/p
    x0[o+p+2:end] .= o>0 ? 0.01/q : 0.05/q
    x0[1] = var(data)*(one(T)-sum(x0[2:o+1])/2-sum(x0[o+2:end]))
	mask = subsetmask(TT, subset)
	x0long = zeros(T, length(mask))
	x0long[mask] .= x0
    return x0long
end

function constraints(::Type{<:TGARCH{o,p,q}}, ::Type{T}) where {o,p, q, T}
    lower = zeros(T, o+p+q+1)
    upper = ones(T, o+p+q+1)

@@ -128,3 +143,36 @@

Loading

128	143		names[o+p+2:o+p+q+1] .= (i -> "α"*subscript(i)).([1:q...])
129	144		return names
130	145		end
	146	+
	147	+	@inline function subsetmask(VS_large::Union{Type{TGARCH{o, p, q}}, Type{TGARCH{o, p, q, T}}}, subs) where {o, p, q, T}
	148	+	ind = falses(nparams(VS_large))
	149	+	subset = zeros(Int, 3)
	150	+	subset[4-length(subs):end] .= subs
	151	+	ind[1] = true
	152	+	os = subset[1]
	153	+	ps = subset[2]
	154	+	qs = subset[3]
	155	+	@assert os <= o
	156	+	@assert ps <= p
	157	+	@assert qs <= q
	158	+	ind[2:2+os-1] .= true
	159	+	ind[2+o:2+o+ps-1] .= true
	160	+	ind[2+o+p:2+o+p+qs-1] .= true
	161	+	ind
	162	+	end
	163	+
	164	+	@inline function subsettuple(VS_large::Union{Type{TGARCH{o, p, q}}, Type{TGARCH{o, p, q, T}}}, subsetmask) where {o, p, q, T}
	165	+	os = 0
	166	+	ps = 0
	167	+	qs = 0
	168	+	@inbounds @simd ivdep for i = 2 : o + 1
	169	+	os += subsetmask[i]
	170	+	end
	171	+	@inbounds @simd ivdep for i = o + 2 : o + p + 1
	172	+	ps += subsetmask[i]
	173	+	end
	174	+	@inbounds @simd ivdep for i = o + p + 2 : o + p + q + 1
	175	+	qs += subsetmask[i]
	176	+	end
	177	+	(os, ps, qs)
	178	+	end

@@ -29,6 +29,7 @@

Loading

EGARCH{o, p, q}(coefs::Vector{T}) where {o, p, q, T}  = EGARCH{o, p, q, T}(coefs)

@inline nparams(::Type{<:EGARCH{o, p, q}}) where {o, p, q} = o+p+q+1
@inline nparams(::Type{<:EGARCH{o, p, q}}, subset) where {o, p, q} = isempty(subset) ? 1 : sum(subset) + 1

@inline presample(::Type{<:EGARCH{o, p, q}}) where {o, p, q} = max(o, p, q)


@@ -70,6 +71,19 @@

Loading

    return x0
end

function startingvals(TT::Type{<:EGARCH}, data::Array{T} , subset::Tuple) where {T}
	o, p, q = subsettuple(TT, subsetmask(TT, subset)) # defend against (p, q) instead of (o, p, q)
	x0 = zeros(T, o+p+q+1)
    x0[2:o+1] .= 0.04/o
    x0[o+2:o+p+1] .= 0.9/p
    x0[o+p+2:end] .= o>0 ? 0.01/q : 0.05/q
    x0[1] = var(data)*(one(T)-sum(x0[2:o+1])/2-sum(x0[o+2:end]))
	mask = subsetmask(TT, subset)
	x0long = zeros(T, length(mask))
	x0long[mask] .= x0
    return x0long
end

function constraints(::Type{<:EGARCH{o, p,q}}, ::Type{T}) where {o, p, q, T}
    lower = zeros(T, o+p+q+1)
    upper = zeros(T, o+p+q+1)

@@ -89,3 +103,36 @@

Loading

89	103		names[o+p+2:o+p+q+1] .= (i -> "α"*subscript(i)).([1:q...])
90	104		return names
91	105		end
	106	+
	107	+	@inline function subsetmask(VS_large::Union{Type{EGARCH{o, p, q}}, Type{EGARCH{o, p, q, T}}}, subs) where {o, p, q, T}
	108	+	ind = falses(nparams(VS_large))
	109	+	subset = zeros(Int, 3)
	110	+	subset[4-length(subs):end] .= subs
	111	+	ind[1] = true
	112	+	os = subset[1]
	113	+	ps = subset[2]
	114	+	qs = subset[3]
	115	+	@assert os <= o
	116	+	@assert ps <= p
	117	+	@assert qs <= q
	118	+	ind[2:2+os-1] .= true
	119	+	ind[2+o:2+o+ps-1] .= true
	120	+	ind[2+o+p:2+o+p+qs-1] .= true
	121	+	ind
	122	+	end
	123	+
	124	+	@inline function subsettuple(VS_large::Union{Type{EGARCH{o, p, q}}, Type{EGARCH{o, p, q, T}}}, subsetmask) where {o, p, q, T}
	125	+	os = 0
	126	+	ps = 0
	127	+	qs = 0
	128	+	@inbounds @simd ivdep for i = 2 : o + 1
	129	+	os += subsetmask[i]
	130	+	end
	131	+	@inbounds @simd ivdep for i = o + 2 : o + p + 1
	132	+	ps += subsetmask[i]
	133	+	end
	134	+	@inbounds @simd ivdep for i = o + p + 2 : o + p + q + 1
	135	+	qs += subsetmask[i]
	136	+	end
	137	+	(os, ps, qs)
	138	+	end

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Hiding 3 contexual commits

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fix table

s-broda

a3ec3b4

Files		•	•	•	Coverage
src	+92	+92	ø	ø	^0.06% 99.41%
Project Totals (12 files)	1,018	1,012	0	6	99.41%

232	273		#but grows them by length(data); hence it should be called with an empty one-
233	274		#dimensional array of the right type.
234	275		@inline function loglik!(ht::AbstractVector{T2}, lht::AbstractVector{T2},
235		-	zt::AbstractVector{T2}, at::AbstractVector{T2}, ::Type{VS}, ::Type{SD}, meanspec::MS,
236		-	data::Vector{T1}, coefs::AbstractVector{T3}
	276	+	zt::AbstractVector{T2}, at::AbstractVector{T2}, vs::Type{VS}, ::Type{SD}, meanspec::MS,
	277	+	data::Vector{T1}, coefs::AbstractVector{T3}, subsetmask=trues(nparams(vs))
237	278		) where {VS<:UnivariateVolatilitySpec, SD<:StandardizedDistribution,
238	279		MS<:MeanSpec, T1<:AbstractFloat, T2, T3
239	280		}
240	281		garchcoefs, distcoefs, meancoefs = splitcoefs(coefs, VS, SD, meanspec)
241		-	#the below 6 lines can be removed when using Fminbox
242		-	lowergarch, uppergarch = constraints(VS, T1)
243		-	lowerdist, upperdist = constraints(SD, T1)
	282	+	lowergarch, uppergarch = constraints(VS, T1)
	283	+	lowerdist, upperdist = constraints(SD, T1)
244	284		lowermean, uppermean = constraints(MS, T1)
245		-	lower = vcat(lowergarch, lowerdist, lowermean)
246		-	upper = vcat(uppergarch, upperdist, uppermean)
247		-	all(lower.<coefs.<upper) \|\| return T2(-Inf)
	285	+	all(lowerdist.<distcoefs.<upperdist) && all(lowermean.<meancoefs.<uppermean) && all(lowergarch[subsetmask].<garchcoefs[subsetmask].<uppergarch[subsetmask]) \|\| return T2(-Inf)
	286	+	garchcoefs .*= subsetmask
248	287		T = length(data)
249	288		r1 = presample(VS)
250	289		r2 = presample(meanspec)

280	319		end#function
281	320
282	321		function loglik(spec::Type{VS}, dist::Type{SD}, meanspec::MS,
283		-	data::Vector{<:AbstractFloat}, coefs::AbstractVector{T2}
	322	+	data::Vector{<:AbstractFloat}, coefs::AbstractVector{T2}, subsetmask=trues(nparams(spec))
284	323		) where {VS<:UnivariateVolatilitySpec, SD<:StandardizedDistribution,
285	324		MS<:MeanSpec, T2
286	325		}

290	329		lht = CircularBuffer{T2}(r)
291	330		zt = CircularBuffer{T2}(r)
292	331		at = CircularBuffer{T2}(r)
293		-	loglik!(ht, lht, zt, at, spec, dist, meanspec, data, coefs)
	332	+	loglik!(ht, lht, zt, at, spec, dist, meanspec, data, coefs, subsetmask)
294	333
295	334		end
296	335

325	364		}
326	365		obj = x -> -loglik(VS, SD, meanspec, data, x)
327	366		coefs = vcat(garchcoefs, distcoefs, meancoefs)
328		-	#for fminbox:
329		-	# lowergarch, uppergarch = constraints(VS, T)
330		-	# lowerdist, upperdist = constraints(SD, T)
331		-	# lowermean, uppermean = constraints(MS, T)
332		-	# lower = vcat(lowergarch, lowerdist, lowermean)
333		-	# upper = vcat(uppergarch, upperdist, uppermean)
334		-	# res = optimize(obj, lower, upper, coefs, Fminbox(algorithm); autodiff=autodiff, kwargs...)
335	367		res = optimize(obj, coefs, algorithm; autodiff=autodiff, kwargs...)
336	368		coefs .= Optim.minimizer(res)
337	369		ng = nparams(VS)

475	535		mylock=Threads.ReentrantLock()
476	536		ndims = max(my_unwrap_unionall(VS)-1, 0) # e.g., two (p and q) for GARCH{p, q, T}
477	537		ndims2 = max(my_unwrap_unionall(MS)-1, 0 )# e.g., two (p and q) for ARMA{p, q, T}
478		-	res = Array{UnivariateARCHModel, ndims+ndims2}(undef, ntuple(i->maxlags - minlags + 1, ndims+ndims2))
	538	+	res = Array{UnivariateSubsetARCHModel, ndims+ndims2}(undef, ntuple(i->maxlags - minlags + 1, ndims+ndims2))
479	539		Threads.@threads for ind in collect(CartesianIndices(size(res)))
480		-	VSi = VS{ind.I[1:ndims] .+ minlags .-1...}
481		-	MSi = (ndims2==0 ? meanspec : meanspec{ind.I[ndims+1:end] .+ minlags .- 1...})
482		-	res[ind] = fit(VSi, data; dist=dist, meanspec=MSi,
	540	+	tup = (ind.I[1:ndims] .+ minlags .-1)
	541	+	MSi = (ndims2==0 ? deepcopy(meanspec) : meanspec{ind.I[ndims+1:end] .+ minlags .- 1...})
	542	+	res[ind] = fitsubset(VS, data, maxlags, tup; dist=dist, meanspec=MSi,
483	543		algorithm=algorithm, autodiff=autodiff, kwargs...)
484	544		if show_trace
485	545		lock(mylock)
	546	+	VSi = VS{tup...}
486	547		Core.print(modname(VSi))
487	548		ndims2>0 && Core.print("-", modname(MSi))
488	549		Core.println(" model has ",

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41	41		end
42	42		end
43	43
	44	+	mutable struct UnivariateSubsetARCHModel{T<:AbstractFloat,
	45	+	VS<:UnivariateVolatilitySpec,
	46	+	SD<:StandardizedDistribution{T},
	47	+	MS<:MeanSpec{T},
	48	+	N
	49	+	} <: ARCHModel
	50	+	spec::VS
	51	+	data::Vector{T}
	52	+	dist::SD
	53	+	meanspec::MS
	54	+	fitted::Bool
	55	+	subset::NTuple{N, Int}
	56	+	function UnivariateSubsetARCHModel{T, VS, SD, MS, N}(spec, data, dist, meanspec, fitted, subset) where {T, VS, SD, MS, N}
	57	+	new(spec, data, dist, meanspec, fitted, subset)
	58	+	end
	59	+	end
	60	+
44	61		"""
45	62		UnivariateARCHModel(spec::UnivariateVolatilitySpec, data::Vector; dist=StdNormal(),
46	63		meanspec=NoIntercept(), fitted=false

75	92		UnivariateARCHModel{T, VS, SD, MS}(spec, data, dist, meanspec, fitted)
76	93		end
77	94
	95	+	function UnivariateSubsetARCHModel(spec::VS,
	96	+	data::Vector{T};
	97	+	dist::SD=StdNormal{T}(),
	98	+	meanspec::MS=NoIntercept{T}(),
	99	+	fitted::Bool=false,
	100	+	subset::NTuple{N, Int}
	101	+	) where {T<:AbstractFloat,
	102	+	VS<:UnivariateVolatilitySpec,
	103	+	SD<:StandardizedDistribution,
	104	+	MS<:MeanSpec,
	105	+	N
	106	+	}
	107	+	UnivariateSubsetARCHModel{T, VS, SD, MS, N}(spec, data, dist, meanspec, fitted, subset)
	108	+	end
78	109		loglikelihood(am::UnivariateARCHModel) = loglik(typeof(am.spec), typeof(am.dist),
79	110		am.meanspec, am.data,
80	111		vcat(am.spec.coefs, am.dist.coefs,
81	112		am.meanspec.coefs
82	113		)
83	114		)
84	115
	116	+	loglikelihood(am::UnivariateSubsetARCHModel) = loglik(typeof(am.spec), typeof(am.dist),
	117	+	am.meanspec, am.data,
	118	+	vcat(am.spec.coefs, am.dist.coefs,
	119	+	am.meanspec.coefs
	120	+	),
	121	+	subsetmask(typeof(am.spec), am.subset)
	122	+	)
	123	+
	124	+
85	125		dof(am::UnivariateARCHModel) = nparams(typeof(am.spec)) + nparams(typeof(am.dist)) + nparams(typeof(am.meanspec))
	126	+	dof(am::UnivariateSubsetARCHModel) = nparams(typeof(am.spec), am.subset) + nparams(typeof(am.dist)) + nparams(typeof(am.meanspec))
86	127		coef(am::UnivariateARCHModel)=vcat(am.spec.coefs, am.dist.coefs, am.meanspec.coefs)
87	128		coefnames(am::UnivariateARCHModel) = vcat(coefnames(typeof(am.spec)),
88	129		coefnames(typeof(am.dist)),

494	555		end
495	556		crits = criterion.(res)
496	557		_, ind = findmin(crits)
497		-	return res[ind]
	558	+	return fit(VS{res[ind].subset...}, data; dist=dist, meanspec=res[ind].meanspec, algorithm=algorithm, autodiff=autodiff, kwargs...)
498	559		end
499	560
500		-
501	561		function coeftable(am::UnivariateARCHModel)
502	562		cc = coef(am)
503	563		se = stderror(am)

71	71		const ARCH = GARCH{0}
72	72
73	73		@inline nparams(::Type{<:TGARCH{o, p, q}}) where {o, p, q} = o+p+q+1
	74	+	@inline nparams(::Type{<:TGARCH{o, p, q}}, subset) where {o, p, q} = isempty(subset) ? 1 : sum(subset) + 1
74	75
75	76		@inline presample(::Type{<:TGARCH{o, p, q}}) where {o, p, q} = max(o, p, q)
76	77
	78	+
77	79		Base.@propagate_inbounds @inline function update!(
78	80		ht, lht, zt, at, ::Type{<:TGARCH{o, p, q}}, garchcoefs
79	81		) where {o, p, q}

112	114		return x0
113	115		end
114	116
	117	+	function startingvals(TT::Type{<:TGARCH}, data::Array{T} , subset::Tuple) where {T}
	118	+	o, p, q = subsettuple(TT, subsetmask(TT, subset)) # defend against (p, q) instead of (o, p, q)
	119	+	x0 = zeros(T, o+p+q+1)
	120	+	x0[2:o+1] .= 0.04/o
	121	+	x0[o+2:o+p+1] .= 0.9/p
	122	+	x0[o+p+2:end] .= o>0 ? 0.01/q : 0.05/q
	123	+	x0[1] = var(data)*(one(T)-sum(x0[2:o+1])/2-sum(x0[o+2:end]))
	124	+	mask = subsetmask(TT, subset)
	125	+	x0long = zeros(T, length(mask))
	126	+	x0long[mask] .= x0
	127	+	return x0long
	128	+	end
	129	+
115	130		function constraints(::Type{<:TGARCH{o,p,q}}, ::Type{T}) where {o,p, q, T}
116	131		lower = zeros(T, o+p+q+1)
117	132		upper = ones(T, o+p+q+1)

29	29		EGARCH{o, p, q}(coefs::Vector{T}) where {o, p, q, T} = EGARCH{o, p, q, T}(coefs)
30	30
31	31		@inline nparams(::Type{<:EGARCH{o, p, q}}) where {o, p, q} = o+p+q+1
	32	+	@inline nparams(::Type{<:EGARCH{o, p, q}}, subset) where {o, p, q} = isempty(subset) ? 1 : sum(subset) + 1
32	33
33	34		@inline presample(::Type{<:EGARCH{o, p, q}}) where {o, p, q} = max(o, p, q)
34	35

70	71		return x0
71	72		end
72	73
	74	+	function startingvals(TT::Type{<:EGARCH}, data::Array{T} , subset::Tuple) where {T}
	75	+	o, p, q = subsettuple(TT, subsetmask(TT, subset)) # defend against (p, q) instead of (o, p, q)
	76	+	x0 = zeros(T, o+p+q+1)
	77	+	x0[2:o+1] .= 0.04/o
	78	+	x0[o+2:o+p+1] .= 0.9/p
	79	+	x0[o+p+2:end] .= o>0 ? 0.01/q : 0.05/q
	80	+	x0[1] = var(data)*(one(T)-sum(x0[2:o+1])/2-sum(x0[o+2:end]))
	81	+	mask = subsetmask(TT, subset)
	82	+	x0long = zeros(T, length(mask))
	83	+	x0long[mask] .= x0
	84	+	return x0long
	85	+	end
	86	+
73	87		function constraints(::Type{<:EGARCH{o, p,q}}, ::Type{T}) where {o, p, q, T}
74	88		lower = zeros(T, o+p+q+1)
75	89		upper = zeros(T, o+p+q+1)

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