a Panel regression in Python

Question

I'm trying to run a panel regression on pandas Dataframes:

Currently I have two dataframes each containing 52 rows(dates)*99 columns(99stocks) :Markdown file with data representation

When running:

est=sm.OLS(Stockslist,averages).fit()
est.summary()

I get the ValueError: shapes (52,99) and (52,99) not aligned: 99 (dim 1) != 52 (dim 0)

Can somebody point me out what I am doing wrong? The model is simply y(i,t)=x(i,t)+error term so no intercept. However I would like to add time effects in the future.

Kind regards, Jeroen

Answer 1

Try the below - I've copied the stock data from the above link and added random data for the x column. For a panel regression you need a 'MultiIndex' as mentioned in the comments.

df = pd.DataFrame(df.set_index('dates').stack())
df.columns = ['y']
df['x'] = np.random.random(size=len(df.index))
df.info()

MultiIndex: 100 entries, (2015-04-03 00:00:00, AB INBEV) to (2015-05-01 00:00:00, ZC.PA)
Data columns (total 2 columns):
y    100 non-null float64
x    100 non-null float64
dtypes: float64(2)
memory usage: 2.3+ KB

regression = PanelOLS(y=df['y'], x=df[['x']])

regression

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x> + <intercept>

Number of Observations:         100
Number of Degrees of Freedom:   2

R-squared:         0.0042
Adj R-squared:    -0.0060

Rmse:              0.2259

F-stat (1, 98):     0.4086, p-value:     0.5242

Degrees of Freedom: model 1, resid 98

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x    -0.0507     0.0794      -0.64     0.5242    -0.2063     0.1048
     intercept     2.1952     0.0448      49.05     0.0000     2.1075     2.2829
---------------------------------End of Summary---------------------------------