stata 命令有误，或者没有安装正确的空间计量经济模型的程序包，导致模型的计算结果有误。
解释:The likelihood is the product of the density evaluated at the observations. Usually, the density takes values that are smaller than one, so its logarithm will be negative. However, this is not true for every distribution. 一般是越大越好,通常结果还会给出-2*log-likelihood,这个值应该越小越...
Iteration 0: Log-likelihood = 1681.6721 Iteration 1: Log-likelihood = 1748.5979 Iteration 2: Log-likelihood = 1755.5589 Iteration 3: Log-likelihood = 1755.9221 Iteration 4: Log-likelihood = 1755.9224 Iteration 5: Log-likelihood = 1755.9224 Computing marginal effects standard errors using MC simulation...
用stata做logit回归,Log likelihood 值为多少比较正常?对300多个数据做回归,十几个变量,log likelihood是负的一百多 答案 -100多可以的,如果significant的话问题不大 结果二 题目 用stata做logit回归,Log likelihood 值为多少比较正常?说明什么问题?对300多个数据做回归,十几个变量,log likelihood是负的一百多 答案...
若在Stata中构建这两种模型后发现loglikelihood值相同，可能原因如下：数据集缺乏时间维度或时间跨度较短，导致模型中的滞后项效应不显著，动态杜宾模型则退化为静态杜宾模型。数据存在多重共线性问题或空间权重矩阵选择不当，影响参数估计准确度，进而使得两种模型的对数似然函数值相同。Stata命令输入错误或未...
stata做ologit怎么输出log-likelihood 一般用ttest. . sysuse auto . ttest mpg==20 . webuse fuel3 . ttest mpg, by(treated) . webuse fuel . ttest mpg1==mpg2 // (immediate form; n=24, m=62.6, sd=15.8; test m=75) . ttesti 24 62.6 15.8 75 test有不同的用法
Iteration 1: Log-likelihood = 1562.4899 Iteration 2: Log-likelihood = 1617.9469 Iteration 3: Log-likelihood = 1640.9464 Iteration 4: Log-likelihood = 1645.2944 Iteration 5: Log-likelihood = 1645.3607 Iteration 6: Log-likelihood = 1645.3607 SAC with spatial fixed-effects Number of obs = 816Group ...
Log-likelihood = 31.1740 --- lninvest | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---+--- Main | lnmvalue | .3087483 .0771167 4.00 0.000 .1576024 .4598942 lnkstock | .0788984 .0306623 2.57 0.010 .0188015 .1389953 ---...
Iteration1:loglikelihood=-2098.5822 StructuralequationmodelNumberofobs=200 Estimationmethod:ml Loglikelihood=-2098.5822 --- |OIM |Coefficientstd.err.zP>|z|[95%conf.interval] ---+--- Structural| read| math|.724807.057982412.500.000.6111636.8384504...
log likelihood是对数似然函数值，它是最大似然估量，跟你这里没有关系。异方差性在logit模型当中是无法避免的问题，可以证明方差为1/NP(1-P)，所以你如果要符合标准的做必须用WLS，加权最小二乘法，一般很少有人在文献里会去用罢了因为繁琐。直接用怀特稳健估计就行。x1等变量都比较显著。详情...