How to Use Lagged Time-Series Variables in a Python Pandas Regression Model?

Question

I'm creating time-series econometric regression models. The data is stored in a Pandas data frame.

How can I do lagged time-series econometric analysis using Python? I have used Eviews in the past (which is a standalone econometric program i.e. not a Python package). To estimate an OLS equation using Eviews you can write something like:

equation eq1.ls log(usales) c log(usales(-1)) log(price(-1)) tv_spend radio_spend

Note the lagged dependent and lagged price terms. It's these lagged variables which seem to be difficult to handle using Python e.g. using scikit or statmodels (unless I've missed something).

Once I've created a model I'd like to perform tests and use the model to forecast.

I'm not interested in doing ARIMA, Exponential Smoothing, or Holt Winters time-series projections - I'm mainly interested in time-series OLS.

Answer 1

pandas allows you to shift your data without moving the index such has

df.shift(-1)

will create a 1 index lag behing

or

df.shift(1)

will create a forward lag of 1 index

so if you have a daily time series, you could use df.shift(1) to create a 1 day lag in you values of price such has

df['lagprice'] = df['price'].shift(1)

after that if you want to do OLS you can look at scipy module here :

http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.linregress.html