我现在粗糙的解决了一下，可以根据生成的_weight、_support和_treat变量来挑选哪些变量匹配成功了，但是这样比较粗糙，没法具体到某一个数据对应的是哪几个

我现在粗糙的解决了一下，可以根据生成的_weight、_support和_treat变量来挑选哪些变量匹配成功了，但是这样比较粗糙，没法具体到某一个数据对应的是哪几个

我现在粗糙的解决了一下，可以根据生成的_weight、_support和_treat变量来挑选哪些变量匹配成功了，但是这样比较粗糙，没法具体到某一个数据对应的是哪几个

抱歉，网卡了一下，发了三条重复的。。

1. 去掉 _weight 缺失的数据是因為那些觀察值沒有被配對 (用) 到。2. 我假設的例子是找尋"兩個" n(2) 最近 propensity score 的觀察值來配對，所以各佔一半的權數 (0.5)，乘以 2，表示其出現之次數；若是 n(1)，就不必乘以 2。3. expand 則是將那些出現次數比 1 次多的重複複製，這樣才符合 1 對 2 之情況！

请问然后怎么实现DID呢

黄老师您好，请问您能推荐一篇psm+did的论文吗，因为psm部分的一些细节不是很能理解，譬如逐年匹配，1:1 还是1:n ，匹配完的数据集如何保存下来 进行did，想folllow一篇相关论文（最好有data和do文件）如能推荐万分感谢，祝好

So, you need the variable which identifies each pair. I haven't used -psmatch2- in a long time and I don't remember how you get that variable. BUt, let's assume you have it and it's called pairid. You also presumably have a subject_id for each person (or firm, or whatever they are). Then you have to account for the pairing as follows:

Code:
mixed y t x1 x2 || pair_id: || subject_id
BUT, there is another problem. You said you want to do a difference in differences analysis. That regression equation doesn't do that. So you also need another variable that indicates pre- and post- onset of treatment status, call it pre_post. Then what you want is:

Code:
mixed y i.t##i.pre_post x1 x2 || pair_id: || subject_id:
The DID estimator of the treatment effect will be the coefficient of 1.t#.pre_post. The best way to understand the results, though, is to look at predicted outcomes in each group both before and after the onset of treatment. -margins t#pre_post- will give you that.

你好，请问如何根据——weight、_support _treated看出配对成功的呢？是_weight=1且support 吗？

类似问题可考虑参加 http://www.peixun.net/view/1377.html，彻底了解 PSM。

比如原来用xtreg y treat year treat*year,fe r,那么现在加一个if _weight!=.即可

黄老师您好，您的代码只是针对最近邻匹配如何加权，那么要是针对其他匹配方法比如核匹配和半径匹配的话，该如何设置权重呢？请您指教~~