Matlab的AIC和BIC的计算方法-关于AIC.doc_matlab中aicbic函数,bicmatlab - CSDN文库

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1.AIC 只需两个 inputs ( LLF , numParams)

2.BIC 需要三个 inputs (LLF , numParams , numObs)

3.aicbic.m 在 garch toolbox 工具箱,AIC,BIC 都容易计算,重点是求 LLF.

*******************

function [AIC , BIC] = aicbic(LLF , numParams , numObs)

%AICBIC Akaike and Bayesian information criteria for model order selection.

% Given optimized log-likelihood function (LLF) values obtained by fitting

% models of the conditional mean and variance to a univariate return series,

% compute the Akaike (AIC) and Bayesian (BIC) information criteria. Since

% information criteria penalize models with additional parameters, AIC and

% BIC are model order selection criteria based on parsimony. When using

% either AIC or BIC, models that minimize the criteria are preferred.

%

% [AIC , BIC] = aicbic(LLF , NumParams , NumObs)

%

% Optional Inputs: NumObs

%

% Inputs:

% LLF - Vector of optimized log-likelihood objective function (LLF)

% values associated with parameter estimates of various models. The LLF

% values are assumed to be obtained from the estimation function GARCHFIT,

% or the inference function GARCHINFER. Type "help garchfit" or "help

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