我的 statsmodels OLS 估计有问题。该模型运行没有任何问题，但是当我尝试调用摘要以便我可以看到实际结果时，当 a 的形状和权重不同时，我得到需要指定的轴的 TypeError 。
我的代码如下所示：
from __future__ import print_function, division
import xlrd as xl
import numpy as np
import scipy as sp
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
file_loc = "/Users/NiklasLindeke/Python/dataset_3.xlsx"
workbook = xl.open_workbook(file_loc)
sheet = workbook.sheet_by_index(0)
tot = sheet.nrows
data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows)]
rv1 = []
rv5 = []
rv22 = []
rv1fcast = []
T = []
price = []
time = []
retnor = []
model = []
for i in range(1, tot):
t = data[i][0]
ret = data[i][1]
ret5 = data[i][2]
ret22 = data[i][3]
ret1_1 = data[i][4]
retn = data[i][5]
t = xl.xldate_as_tuple(t, 0)
rv1.append(ret)
rv5.append(ret5)
rv22.append(ret22)
rv1fcast.append(ret1_1)
retnor.append(retn)
T.append(t)
df = pd.DataFrame({'RVFCAST':rv1fcast, 'RV1':rv1, 'RV5':rv5, 'RV22':rv22,})
df = df[df.RVFCAST != ""]
Model = smf.ols(formula='RVFCAST ~ RV1 + RV5 + RV22', data = df).fit()
print Model.summary()
换句话说，这是行不通的。
回调如下：
print Model.summary()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-394-ea8ea5139fd4> in <module>()
----> 1 print Model.summary()
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.6-x86_64.egg/statsmodels/regression/linear_model.pyc in summary(self, yname, xname, title, alpha)
1948 top_left.append(('Covariance Type:', [self.cov_type]))
1949
-> 1950 top_right = [('R-squared:', ["%#8.3f" % self.rsquared]),
1951 ('Adj. R-squared:', ["%#8.3f" % self.rsquared_adj]),
1952 ('F-statistic:', ["%#8.4g" % self.fvalue] ),
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.6-x86_64.egg/statsmodels/tools/decorators.pyc in __get__(self, obj, type)
92 if _cachedval is None:
93 # Call the "fget" function
---> 94 _cachedval = self.fget(obj)
95 # Set the attribute in obj
96 # print("Setting %s in cache to %s" % (name, _cachedval))
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.6-x86_64.egg/statsmodels/regression/linear_model.pyc in rsquared(self)
1179 def rsquared(self):
1180 if self.k_constant:
-> 1181 return 1 - self.ssr/self.centered_tss
1182 else:
1183 return 1 - self.ssr/self.uncentered_tss
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.6-x86_64.egg/statsmodels/tools/decorators.pyc in __get__(self, obj, type)
92 if _cachedval is None:
93 # Call the "fget" function
---> 94 _cachedval = self.fget(obj)
95 # Set the attribute in obj
96 # print("Setting %s in cache to %s" % (name, _cachedval))
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/statsmodels-0.6.1-py2.7-macosx-10.6-x86_64.egg/statsmodels/regression/linear_model.pyc in centered_tss(self)
1159 if weights is not None:
1160 return np.sum(weights*(model.endog - np.average(model.endog,
-> 1161 weights=weights))**2)
1162 else: # this is probably broken for GLS
1163 centered_endog = model.wendog - model.wendog.mean()
/Users/NiklasLindeke/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/numpy/lib/function_base.pyc in average(a, axis, weights, returned)
522 if axis is None:
523 raise TypeError(
--> 524 "Axis must be specified when shapes of a and weights "
525 "differ.")
526 if wgt.ndim != 1:
TypeError: Axis must be specified when shapes of a and weights differ.
我很抱歉，但我不知道从那里该做什么。我还希望在此之后，使用一些 Newey-West 方法对自相关进行校正，我看到您可以使用以下行来执行此操作：
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)
但是当我尝试用我的模型运行它时，它返回错误：
ValueError: operands could not be broadcast together with shapes (256,766) (256,1,256)
但我意识到 statsmodels.formula 与 get_robustcov 函数不兼容，但如果是这样，我该如何测试自相关呢？
但我最紧迫的问题是我无法为我的 OLS 编写摘要。
根据要求，这是 df 中我的数据集的前三十行。
print df
RV1 RV22 RV5 RVFCAST
0 0.01553801 0.01309511 0.01081393 0.008421236
1 0.008881671 0.01301336 0.01134905 0.01553801
2 0.01042178 0.01326669 0.01189979 0.008881671
3 0.009809431 0.01334593 0.01170942 0.01042178
4 0.009418737 0.01358808 0.01152253 0.009809431
5 0.01821364 0.01362502 0.01269661 0.009418737
6 0.01163536 0.01331585 0.01147541 0.01821364
7 0.009469907 0.01329509 0.01172988 0.01163536
8 0.008875018 0.01361841 0.01202432 0.009469907
9 0.01528914 0.01430873 0.01233219 0.008875018
10 0.01210761 0.01412724 0.01238776 0.01528914
11 0.01290773 0.0144439 0.01432174 0.01210761
12 0.01094212 0.01425895 0.01493865 0.01290773
13 0.01041433 0.01430177 0.0156763 0.01094212
14 0.01556703 0.0142857 0.01986616 0.01041433
15 0.0217775 0.01430253 0.01864532 0.01556703
16 0.01599228 0.01390088 0.01579069 0.0217775
17 0.01463037 0.01384096 0.01416622 0.01599228
18 0.03136361 0.01395866 0.01398807 0.01463037
19 0.009462822 0.01295695 0.0106063 0.03136361
20 0.007504367 0.01295204 0.01114677 0.009462822
21 0.007869922 0.01300863 0.01267322 0.007504367
22 0.01373964 0.0129547 0.01314553 0.007869922
23 0.01445476 0.01271198 0.01268 0.01373964
24 0.01216517 0.01249902 0.01202476 0.01445476
25 0.0151366 0.01266783 0.0129083 0.01216517
26 0.01023149 0.01258627 0.0146934 0.0151366
27 0.01141199 0.01284094 0.01490637 0.01023149
28 0.01117856 0.01321258 0.01643881 0.01141199
29 0.01658287 0.01340074 0.01597086 0.01117856
