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hi this is nick weve seen that the first differenced estimator and the fixed effects estimator both allow us to leverage the panel structure of our data to derive estimators for the betas that are consistent in large samples and unbiased in small samples even if we have some cross-sectional correlation between our explanatory variables and our econometric error term both estimators are derived in a very similar way and so you might be wondering how do they differ when should i use first differences and when should i use fixed effects in this video im going to try to lay out the issues so recall that the first difference to estimator is based on taking a temporal difference of our dependent variable and temporal differences in each of our explanatory variables the fixed effects estimator is based on demeaning our dependent and independent variables in both cases after we transform our original pooled ols specification we estimate the betas by running ols on the transform model and in