Pandas Dataframe AttributeError: 'DataFrame' object has no attribute 'design_info'

I am trying to use the predict() function of the statsmodels.formula.api OLS implementation. When I pass a new data frame to the function to get predicted values for an out-of-sample dataset result.predict(newdf) returns the following error: 'DataFrame' object has no attribute 'design_info'. What does this mean and how do I fix it? The full traceback is:

    p = result.predict(newdf)
  File "C:\Python27\lib\site-packages\statsmodels\base\model.py", line 878, in predict
    exog = dmatrix(self.model.data.orig_exog.design_info.builder,
  File "C:\Python27\lib\site-packages\pandas\core\frame.py", line 2088, in __getattr__
    (type(self).__name__, name))
AttributeError: 'DataFrame' object has no attribute 'design_info'

EDIT: Here is a reproducible example. The error appears to occur when I pickle and then unpickle the result object (which I need to do in my actual project):

import cPickle
import pandas as pd
import numpy as np
import statsmodels.formula.api as sm

df = pd.DataFrame({"A": [10,20,30,324,2353], "B": [20, 30, 10, 1, 2332], "C": [0, -30, 120, 11, 2]})

result = sm.ols(formula="A ~ B + C", data=df).fit()
print result.summary()

test1 = result.predict(df) #works

f_myfile = open('resultobject', "wb")
cPickle.dump(result, f_myfile, 2)
f_myfile.close()
print("Result Object Saved")


f_myfile = open('resultobject', "rb")
model = cPickle.load(f_myfile)

test2 = model.predict(df) #produces error