Panel estimators such the one implemented in the R package plm
allow to estimate "individual"
, "time"
or "twoways"
effects.
See page 11.
When do I use which of the three possible specifications?
Panel estimators such the one implemented in the R package plm
allow to estimate "individual"
, "time"
or "twoways"
effects.
See page 11.
When do I use which of the three possible specifications?
The canonical two-way model is $$ y_{it}=x_{it}'\beta+\alpha_i+\theta_t+\epsilon_{it} $$ Here, the individual effect is $\alpha_i$, and $\theta_t$ is the time effect. It is a two-way model if both are present. Thus, $\alpha_i$ captures effects that are specific to some panel unit but constant over time, whereas $\theta_t$ captures effects that are specific to some time period but constant over panel units.
So, whether you need both will, as @Ben pointed out, depend on your research question. For example, if have a panel of firms, $\theta_t$ might represent business cycle effects, whereas $\alpha_i$ would contain firm specific effects that can be argued to be constant over time, such as the "culture" of the firm.
It depends on your research, in some cases time effects could solve the cross-sectional problem. An article that is very useful is "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches" by Mitchell A. Petersen, 2009.
In fact, twoways here means both individual and time effects, so it is just two specifications
hope this helps