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I am having an issue with my statsmodels OLS estimation. The model runs without any issues, but when I try to call for a summary so that I can see the actual results I get the TypeError of the axis needing to be specified when shapes of a and weights differ.
My code looks like this:
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()
In other words, this doesnt work.
The callback is the following:
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.
Where I am sorry, but I have no idea what to do from there. And I wish to also after this, perform a correction for auto-correlation with some Newey-West method, which I saw you could do with the following line:
mdl = Model.get_robustcov_results(cov_type='HAC',maxlags=1)
But when I try to run that with my model it returns the error:
ValueError: operands could not be broadcast together with shapes (256,766) (256,1,256)
But I realize that the statsmodels.formula isn't compatible with the get_robustcov function, but if so, how could I test for the auto-correlation then?
But my most pressing issue is the fact that I cannot produce a summary for my OLS.
As requested, here is the first thirty rows of my dataset in df.
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
518
I would like to thank user333800 for all the help!
For future reference if anyone comes across the same issue.
The following code:
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)
gave me:
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
==============================================================================
And I can now continue on my paper :)
63
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