急求，请高手不吝赐教，面板数据评价改革前后影响，该如何建立模型

请教各位大侠，有一组关于各省的医疗支出及其影响因素的面板数据以及各省份间公立医院病床占比，数据是从2005-2011年。已知公立医院改革发生在2007年，想评价改革前后公立医院病床占比是否的医疗支出产生了影响。用经典面板回归方程，同时引入公立医院病床占比(perlphbed)*年虚拟(D)变量的交互项. 如下
exphealth=b0+bX+ui+vit+perlphbed*D06+perlphbed*D07...+perlphbed*D11
交互项的意义我借鉴了伍德里奇计量经济学导论例14.2， Has return to education changed over time?这个例子，把教育年限换成了公立医院病床占比。交互项系数表示和2005年相比，该年度1个公立医院病床百分比对医疗支出的贡献是高了还是低了。但我的模型可能有一个问题，伍德里奇的例子里教育年限在研究的期间是不变的，但我的模型里公立医院占比每年都有微小的变化。我直接吧交互项解释为改年和2005年比1%公立医院病床占比的变化合适吗？或者还有什么更好的方法来比较改革前后公立医院病床占比对医疗支出的影响吗？
太感谢了！！！

[Has the Return to Education Changed over Time?]
The data in WAGEPAN.RAW are from Vella and Verbeek (1998). Each of the 545 men in the sample
worked in every year from 1980 through 1987. Some variables in the data set change over time: experience,
marital status, and union status are the three important ones. Other variables do not change:
race and education are the key examples. If we use fixed effects (or first differencing), we cannot
include race, education, or experience in the equation. However, we can include interactions of educ
with year dummies for 1981 through 1987 to test whether the return to education was constant over
this time period. We use log(wage) as the dependent variable, dummy variables for marital and union
status, a full set of year dummies, and the interaction terms d81educ, d82educ, …, d87educ.
The estimates on these interaction terms are all positive, and they generally get larger for more
recent years. The largest coefficient of .030 is on d87educ, with t
2.48. In other words, the return
to education is estimated to be about 3 percentage points larger in 1987 than in the base year, 1980.
(We do not have an estimate of the return to education in the base year for the reasons given earlier.)
The other significant interaction term is d86educ (coefficient
.027, t
2.23). The estimates on
the earlier years are smaller and insignificant at the 5% level against a two-sided alternative. If we
do a joint F test for significance of all seven interaction terms, we get p-value
.28: this gives an
example where a set of variables is jointly insignificant even though some variables are individually
significant. [The df for the F test are 7 and 3,799; the second of these comes from N(T  1)  k

545(8  1)  16
3,799.] Generally, the results are consistent with an increase in the return to
education over this period.

试着用HLM分层线性模型。

南南数据 发表于 2015-2-13 14:55
试着用HLM分层线性模型。

可以详细说一下吗，如果用panel regression的话，我的模型有问题吗？

应该考虑difference-in-differences

soccy 发表于 2015-2-16 20:30
应该考虑difference-in-differences

有一定道理，但是改革是在所有省份都开展的，并没有明显的实验组和对照组，这样的DiD改怎么弄呢？

应该有强度上的区别吧。

或者城乡差异？