Maximum likelihood estimation (越大越好) optimizes likelihood function ( no negative sign).

Here is why the objective function defined as -2 Log likelihood

The log function is monotonic and makes it easy to calculate. Some software do it in minimum that is why there a negative sign(-). My guess is because a regression problem is defined to minimize sum of error square. The number 2 is there for ease for hypothesis testing when one needs it, because 2*( loglikehood ration) is of chi square distribution with # df.

Maximum likelihood estimation (越大越好) optimizes likelihood function ( no negative sign).

Here is why the objective function defined as -2 Log likelihood

The log function is monotonic and makes it easy to calculate. Somesoftware do it in minimum that is why there a negative sign(-). Myguess is because a regression problem is defined to minimize sum oferror square. The number 2 is there for ease for hypothesis testingwhen one needs it, because 2*( loglikehood ration) is of chi squaredistribution with # df.

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理解对数似然估计函数值。对数似然估计函数值一般取负值，实际值（不是绝对值）越大越好。第一，基本推理。对于似然函数，如果是离散分布，最后得到的数值直接就是概率，取值区间为0-1，对数化之后的值就是负数了；如果是连续变量，因为概率密度函数的取值区间并不局限于0-1，所以最后得到的似然函数值不是概率而只是概率密度函数值，这样对数化之后的正负就不确定了。第二，Eviews的计算公式解释。公式值的大小关键取之于残差平方和（以及样本容量），只有当残差平方和与样本容量的比之很小时，括号内的值才可能为负，从而公式值为正，这时说明参数拟合效度很高；反之公式值为负，但其绝对值越小表示残差平方和越小，因而参数拟合效度越高。

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对数似然函数值具体怎么求？

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似然函数值的自然对数的—2倍，常用来反映模型的拟合程度，其值越小，表示拟合程度越好.

似然比统计量，服从卡方分布，是一个检验统计量，无所谓越大越好还是越小越好。该统计量大于卡方临界值时，拒绝原假设，否则接受原假设

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为什么有些说越大越好，有些又是越小越好

可是如果log likelihood是负数怎么求其自然函数啊

如果做clogit分析，得到的log likelihood =1670 该怎麽办呢，而且改变解释变量没有变化

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