How do I use lagged independent variable in statsmodel OLS regression?

If there is good reason to believe that an independent variable (x) has a lagged effect on dependent variable (y) of a OLS regression model.

import statsmodel
import pandas

# Create DataFrame

sDataFrame = pd.DataFrame({
'Time': ['2012-Q1','2012-Q2','2012-Q3','2012-Q4','2013-Q1','2013-Q2'],
'GDP': ['6.1','6.4','6.8','7.1','6.2','5.8'],
'FDI': ['3.2','2.9','3.1','2.5','1.8','2.3'],
'Unemployment': ['12.1','10.3','11.5','12.4','9.8','11.2']
})

My current formula looks something like this:

model = sm.ols(formula = 'GDP ~ FDI + FDI_Lag + Unemployment', data=sDataFrame).fit()
model.summary()


My question is how do I include FDI_Lag variable in my model, which is FDI - 1 i.e the previous value in DataFrame.