Nik*_*eke 6 python finance linear-regression statsmodels
我的statsmodels OLS估算有问题。该模型运行时没有任何问题，但是当我尝试调用摘要时，我可以看到实际结果，当a和权重的形状不同时，我需要指定轴的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，这不起作用。
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.
很抱歉，但是我不知道该怎么办。我还希望在此之后，使用一些Newey-West方法对自相关进行校正，我发现您可以使用以下代码行：
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)
但是，当我尝试用模型运行它时，会返回错误：
ValueError: operands could not be broadcast together with shapes (256,766) (256,1,256)
但是我意识到statsmodels.formula与get_robustcov函数不兼容，但是如果是这样，那我如何测试自相关呢？
但是我最紧迫的问题是我无法为自己的OLS生成摘要。
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
我要感谢 user333800 的所有帮助！
如果有人遇到同样的问题，以供将来参考。
以下代码：
df = pd.DataFrame({'RVFCAST':rv1fcast, 'RV1':rv1, 'RV5':rv5, 'RV22':rv22,})
df = df[df.RVFCAST != ""]
df = df.astype(float)
Model = smf.ols(formula='RVFCAST ~ RV1 + RV5 + RV22', data = df).fit()
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)
给我：
print mdl.summary()
OLS Regression Results
==============================================================================
Dep. Variable: RVFCAST R-squared: 0.681
Model: OLS Adj. R-squared: 0.677
Method: Least Squares F-statistic: 120.9
Date: Wed, 22 Apr 2015 Prob (F-statistic): 1.60e-48
Time: 17:19:19 Log-Likelihood: 1159.8
No. Observations: 256 AIC: -2312.
Df Residuals: 252 BIC: -2297.
Df Model: 3
Covariance Type: HAC
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept 0.0005 0.000 2.285 0.023 7.24e-05 0.001
RV1 0.2823 0.104 2.710 0.007 0.077 0.487
RV5 -0.0486 0.193 -0.252 0.802 -0.429 0.332
RV22 0.7450 0.232 3.212 0.001 0.288 1.202
==============================================================================
Omnibus: 174.186 Durbin-Watson: 2.045
Prob(Omnibus): 0.000 Jarque-Bera (JB): 2152.634
Skew: 2.546 Prob(JB): 0.00
Kurtosis: 16.262 Cond. No. 1.19e+03
==============================================================================
我现在可以继续我的论文了 :)
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