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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.

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If an independent variable (x) has a lagged effect on dependent variable (y) of a OLS regression model, you must insert its lagged value and not current value in time series data. Your proposed stats model includes both current value and lagged value . This is not justifiable. Therefore, correct your model and proceed.

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  • $\begingroup$ I am not sure that is true. There are def models that incorporate current val, and multiple levels of lagged value. $\endgroup$ – kms Sep 26 at 19:16
  • $\begingroup$ Incorporating the same variable twice as independent variables that are auto-correlated can be dangerous in a multiple regression analysis. The variance analysis presumes I.I.D. $\endgroup$ – Subhash C. Davar Sep 26 at 23:23
  • $\begingroup$ It is illogical to presume that GDP is influenced by both current and its lagged levels. $\endgroup$ – Subhash C. Davar Sep 26 at 23:28
  • $\begingroup$ If you are not sure why would you follow a def model ! gross domestic product and foreign direct investment relationship can be better understood from discipline of economics. $\endgroup$ – Subhash C. Davar Sep 26 at 23:35

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