Pandas 和 PanelOLS：仅支持 2 级多索引

Question

I have a dataframe like this:

 year fcode yx 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

I have sorted by year and fcode :

df.sort_index(by=['year','fcode'])

I droped missing data:

df = df.dropna() # Drop missing

I got this:

     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

I try to run this:

model  = pd.stats.plm.PanelOLS(y=df['y'],x=df[['x']],time_effects=True)

I get this error:

raise NotImplementedError('Only 2-level MultiIndex are supported.') NotImplementedError: Only 2-level MultiIndex are supported.

I have no idea what I am doing wrong. You can see that it seems my code is similar to Fixed effects in Pandas

When I add

df=df.set_index('year', append=True)

I get

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

Answer 1

You can try:

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---------------------------------