How are fixed effects and lagged dependent variables different?

How are fixed effects and lagged dependent variables different?

Thanks. The fixed effects and lagged dependent variable models are different models, so can give different results. We discuss this on p. 245-46 in the book. If the results are very different you could consider estimating a model with both fixed effects and a lagged dependent 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 are the independent variables expected to change?

These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect. The variable that is stable and unaffected by the other variables you are trying to measure.

Can a lagged dependent variable be an endogenous variable?

Last edited by David Chin; 25 Apr 2016, 20:23 . I think you’re making problems where they don’t exist. If the lagged variable is endogenous (e.g., a lagged dependent variable) then you do have problems.

Is there bias in LDV and Fe estimators?

LDV and FE estimators bound the causal effect of interest (Angrist and Pischke 2009, 246). If lagged dependent variable and fixed effect are both included then there is bias. Though this bias is not too bad and declines with the amount of data (CITE?).

What’s the difference between first differencing and fixed effects?

First Difference. First differencing is an alternative to fixed effects. It also achieves the same goal: to eliminate from the model. If is related to the the treatment effect, first differencing will also yield an unbiased estimate of the effect. The original equation admits a separate equations for each drug.

Is the difference in difference estimator similar to the fixed effect model?

Since a fixed effect approach can usually be turned into a difference-in-difference approach by including period level dummies, there is often little reason not to do a DiD. The difference-in-difference estimator is similar to the fixed effect 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 lags included in a dynamic theory?

I recommend two articles: Keele, L. and Kelly N. J. (2005) Dynamic models for dynamic theories: the ins and outs of lagged dependent variables ( link ). The upshot is that including a lagged dependent variable can have a large influence on the coefficients of the remaining variables.