我有一个这样的数据框：
year fcode y x 0 1987 410032 NaN 0 1 1988 410032 NaN 0 2 1989 410032 NaN 0 3 1987 410440 NaN 0 4 1988 410440 NaN 0 5 1989 410440 NaN 0 6 1987 410495 NaN 0 7 1988 410495 NaN 0 8 1989 410495 NaN 0 9 1987 410500 NaN 0 10 1988 410500 NaN 0 11 1989 410500 NaN 0 12 1987 410501 NaN 0 13 1988 410501 NaN 0 14 1989 410501 NaN 0 15 1987 410509 NaN 0 16 1988 410509 NaN 0 17 1989 410509 NaN 0 18 1987 410513 NaN 0 19 1988 410513 NaN 0 20 1989 410513 NaN 0 21 1987 410517 NaN 0 22 1988 410517 NaN 0 23 1989 410517 NaN 0 24 1987 410518 NaN 0 25 1988 410518 NaN 0 26 1989 410518 NaN 0 27 1987 410521 NaN 0 28 1988 410521 NaN 0 29 1989 410521 NaN 0 .. ... ... ... ... 441 1987 419450 NaN 0 442 1988 419450 NaN 0 443 1989 419450 NaN 0 444 1987 419459 0.512824 0 445 1988 419459 0.916291 0 446 1989 419459 0.113329 0
我按year和fcode排序：
df.sort_index(by=['year','fcode'])
我删除了丢失的数据：
df = df.dropna() # Drop missing
我明白了：
year fcode y x
30 1987 410523 -2.813411 0
48 1987 410538 0.970779 0
75 1987 410563 1.791759 0
81 1987 410565 3.044523 0
84 1987 410566 1.945910 0
87 1987 410567 0.000000 0
96 1987 410577 0.518794 0
105 1987 410592 3.401197 0
108 1987 410593 0.000000 0
111 1987 410596 2.302585 0
120 1987 410606 -0.415515 0
129 1987 410626 -0.139262 0
135 1987 410629 0.182322 0
159 1987 410653 0.058269 0
162 1987 410665 -2.995732 0
171 1987 410685 -1.966113 0
186 1987 418011 2.302585 0
195 1987 418021 0.000000 0
201 1987 418035 1.791759 0
207 1987 418045 0.693147 0
213 1987 418051 -0.798508 0
219 1987 418054 0.223143 0
222 1987 418065 0.262364 0
228 1987 418076 0.058269 0
231 1987 418083 1.098612 0
237 1987 418091 2.101692 0
240 1987 418097 0.512824 0
246 1987 418107 -0.020203 0
252 1987 418118 0.000000 0
258 1987 418125 -0.798508 0
... ... ... ...
233 1989 418083 0.000000 0
239 1989 418091 -0.579819 0
242 1989 418097 0.350657 0
248 1989 418107 -0.798508 0
254 1989 418118 -2.302585 0
260 1989 418125 -0.510826 0
266 1989 418140 0.916291 0
272 1989 418163 1.871802 0
275 1989 418168 -1.609438 0
278 1989 418177 2.890372 0
299 1989 418237 -1.660731 0
311 1989 419198 1.386294 0
314 1989 419201 0.693147 0
317 1989 419242 1.740466 0
320 1989 419268 -0.105360 1
323 1989 419272 2.833213 1
332 1989 419289 -0.051293 1
335 1989 419297 -1.309333 0
350 1989 419307 -0.116534 1
368 1989 419339 -0.798508 0
371 1989 419343 1.098612 1
383 1989 419357 -0.693147 1
392 1989 419378 0.292670 1
401 1989 419381 -0.967584 1
407 1989 419388 1.791759 1
422 1989 419409 0.693147 1
431 1989 419432 1.648659 0
446 1989 419459 0.113329 0
464 1989 419482 1.029619 0
467 1989 419483 3.401197 0
我尝试运行：
model = pd.stats.plm.PanelOLS(y=df['y'],x=df[['x']],time_effects=True)
我收到此错误：
引发NotImplementedError（＆＃39;仅支持2级MultiIndex。＆＃39;） NotImplementedError：仅支持2级MultiIndex。
我不知道我做错了什么。您可以看到我的代码似乎与Fixed effects in Pandas
相似当我添加
df=df.set_index('year', append=True)
我得到了
Degrees of Freedom: model 161, resid 0
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x 0.0000 nan nan nan nan nan
答案 0 :(得分：1)
您可以尝试：
print df.head()
year fcode y x
30 1987 410523 -2.813411 0
48 1987 410538 0.970779 0
75 1987 410563 1.791759 0
81 1987 410565 3.044523 0
84 1987 410566 1.945910 0
#convert year to datetime
df['year'] = pd.to_datetime(df['year'], format='%Y')
#add column year to index
df=df.set_index('year', append=True)
#swap indexes
df.index = df.index.swaplevel(0,1)
print df.head()
fcode y x
year
1987-01-01 30 410523 -2.813411 0
48 410538 0.970779 0
75 410563 1.791759 0
81 410565 3.044523 0
84 410566 1.945910 0
model = pd.stats.plm.PanelOLS(y=df['y'],x=df[['x']],time_effects=True)
print model
-------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x>
Number of Observations: 60
Number of Degrees of Freedom: 3
R-squared: 0.0013
Adj R-squared: -0.0338
Rmse: 1.4727
F-stat (1, 57): 0.0364, p-value: 0.8493
Degrees of Freedom: model 2, resid 57
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x 0.1539 0.5704 0.27 0.7882 -0.9640 1.2719
---------------------------------End of Summary---------------------------------