Why include lagged variables?
Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process.
What are lags in regression?
In statistics and econometrics, a distributed lag model is a model for time series data in which a regression equation is used to predict current values of a dependent variable based on both the current values of an explanatory variable and the lagged (past period) values of this explanatory variable.
Is it legitimate to include a lagged dependent variable in a regression model?
I’m very confused about if it’s legitimate to include a lagged dependent variable into a regression model.
Why is it important to include lags in a model?
As others have said, it’s important to think about the process being modelled. Including lagged dependent variables can reduce the occurrence of autocorrelation arising from model misspecification. Thus accounting for lagged dependent variables helps you to defend the existence of autocorrelation in the model.
How are regressions with time series variables affected?
1. Regressions with time series variables involve two issues we have not dealt with in the past. First, one variable can influence another with a time lag. Second, if the variables are non-stationary, the spurious regressions problem can result. The latter issue will be dealt with later on. 2. Distributed lag models have the dependent
Why do we need to account for lagged dependent variables?
Thus accounting for lagged dependent variables helps you to defend the existence of autocorrelation in the model. The past value affects the present in the model, requires theoretical foundation, and best fit up the model as per required.