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2016-11-22 回答
BIC需要三个 inputs (LLF , numParams , numObs)
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%AICBIC Akaike and Bayesian information criteria for model order selection.
% models of the conditional mean and variance to a univariate return series,
% information criteria penalize models with additional parameters, AIC and
% either AIC or BIC, models that minimize the criteria are preferred.
% [AIC , BIC] = aicbic(LLF , NumParams , NumObs)
% Optional Inputs: NumObs
% Inputs:
% values associated with parameter estimates of various models. The LLF
% or the inference function GARCHINFER. Type “help garchfit” or “help
% garchinfer” for details.
% NumParams – Number of estimated parameters associated with each value
% vector the same length as LLF. All elements of NumParams must be
% GARCHCOUNT. Type “help garchcount” for details.
% Optional Input:
% value of LLF. NumObs is required for computing BIC, but is not needed
% vector the same length as LLF. All elements NumObs must be positive
%
% AIC – Vector of AIC statistics associated with each LLF objective
%
%
% function value. The BIC statistic is defined as:
% BIC = -2*LLF + NumParams*Log(NumObs)
%example
load garchdata
[m,n]=size(dem2gbp); %[1974,1]
spec11 = garchset(‘P’,1,’Q’,1,’Display’,’off’);
garchdisp(coeff11,errors11)
format long
[AIC,BIC]
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