First-difference estimator
In statistics and econometrics, the first-difference (FD) estimator is an estimator used to address the problem of omitted variables with panel data. It is consistent under the assumptions of the fixed effects model. In certain situations it can be more efficient than the standard fixed effects (or "within") estimator.
The estimator requires data on a dependent variable, , and independent variables, , for a set of individual units and time periods The estimator is obtained by running a pooled ordinary least squares (OLS) estimation for a regression of on .
Derivation
The FD estimator avoids bias due to some omitted, time-invariant variable using the repeated observations over time:
Differencing both equations, gives:
which removes the unobserved .
The FD estimator is then simply obtained by regressing changes on changes using OLS:
Note that the rank condition must be met for to be invertible ().
Similarly,
where is given by
Properties
Under the assumption of , the FD estimator is unbiased and consistent. Note that this assumption is less restrictive than the assumption of strict exogeneity required for unbiasedness using the fixed effects (FE) estimator. If the disturbance term follows a random walk, the usual OLS standard errors are asymptotically valid.
Relation to fixed effects estimator
For , the FD and fixed effects estimators are numerically equivalent.
Under the assumption of homoscedasticity and no serial correlation in , the FE estimator is more efficient than the FD estimator. If follows a random walk, however, the FD estimator is more efficient as are serially uncorrelated.
See also
References
- Wooldridge, Jeffrey M. (2001). Econometric Analysis of Cross Section and Panel Data. MIT Press. pp. 279–291. ISBN 978-0-262-23219-7.