Hi Delaney

In the MLR lecture I can't work out why running slr.params[1] is returning 2.78 but when printing summary.results() it shows X2 coef is 1.

Is the coef printed on the summary screen not reflecting the beta?

I noticed the same problem further on when running the MLR using a benchmark whereby using the results.summary () and mlr.summary () are showing different values for the X1 and X2 coef.

Additionally, the example at the end of the lecture using X1...X4 has the beta line up with the coef that is printed on results.summary (). Not sure why I'm getting conflicting results or if I'm simply interpreting it incorrectly.

Code for SLR:
start = '2014-01-01'
end = '2015-01-01'
asset1 = get_pricing('AAPL', fields='price', start_date=start, end_date=end)
asset2 = get_pricing('FISV', fields='price', start_date=start, end_date=end)
benchmark = get_pricing('SPY', fields='price', start_date=start, end_date=end)

slr = regression.linear_model.OLS(asset1, sm.add_constant(asset2)).fit()
print 'SLR beta of asset2:', slr.params[1]
print results.summary()

Code for MLR:
mlr = regression.linear_model.OLS(asset1, sm.add_constant(np.column_stack((asset2, benchmark)))).fit()

prediction = mlr.params[0] + mlr.params[1]*asset2 + mlr.params[2]*benchmark
prediction.name = 'Prediction'

print 'MLR beta of asset2:', mlr.params[1], '\nMLR beta of S&P 500:', mlr.params[2]
print results.summary()
mlr.summary()

Hi Chris,

If I am interpreting your issue correctly, it seems to be that the problem is with the regression that is stored in the results variable, right? We define results as a regression like so:

X1 = np.arange(100)  
X2 = np.array([i ** 2 for i in range(100)]) + X1  
Y = X1 + X2

X = sm.add_constant( np.column_stack( (X1, X2) ) )  
results = regression.linear_model.OLS(Y, X).fit()  

So the coefficient of 1 makes sense, simply because this is a toy example. The slr and mlr regression variables contain examples with actual pricing data so it makes sense their summaries should be out of line with that of results. Hope this helps!

Ohhh I see. If I understand, X1 and X2 in the results.summary printout actually represent the X1 and X2 dummy variables that we assigned earlier?

I had thought that they represented 'factor 1' and 'factor 2' in our mlr which would have been asset 2 and the benchmark respectively.

Precisely so, yes. If you want the factors from mlr, the multiple linear regression with actual data, those are represented by x1 and x2 in mlr.summary() and can be accessed more directly with mlr.params. The results variable only holds our dummy regression from before.

Ok perfect thank you

How can one regress one non-stationary price time series on another. The results won;'t make sense

@Dinesh, absolutely. Unless they are cointegrated.

That's right, the series must be stationary for the regression to give useful results. Note that this will not show in any of the statistics, in fact in the presence of non-stationarity confidence can be overestimated. We discuss this here: https://www.quantopian.com/lectures#Violations-of-Regression-Models