fixed-effect model

(redirected from Fixed effects estimator)
Also found in: Wikipedia.

fixed-effect model

A statistical model that stipulates that the units being analysed—e.g. people in a trial or studies in a meta-analysis—are the ones of interest, and thus constitute the entire population of units. Only within-study variation is taken to influence the uncertainty of results (as reflected in the confidence interval) of a meta-analysis using a fixed-effect model. Variation between the estimates of effect from each study (heterogeneity) does not affect the confidence interval in a fixed-effect model (Cochrane definition).
Mentioned in ?
References in periodicals archive ?
At one extreme, the mean-group estimator allows for complete heterogeneity while the dynamic fixed effects estimator imposes parameter homogeneity across all countries.
The fixed effects estimator assumes Uf is correlated with the independent variables.
If the effects are correlated with the explanatory variables, the fixed effects estimator is consistent and efficient but the random effect is now inconsistent.
12) In contrast, the fixed effects estimator is always consistent and unbiased.
The fixed effects estimator correctly infers that assets do not rise at any age for individual i and thus [[beta].
The fixed effects estimator could not be used with these variables since they include no intertemporal variation.
Second, the IV-GLS estimator should be more efficient than the fixed effects estimator.
The fixed effects estimator assumes that the influence of year and industry group on the likelihood of operating a single-parent captive is the same for all the corporations in the sample.
The fixed effects estimator is a consistent estimator, even when individual time-fixed heterogeneity, [c.
If this assumption is valid, the fixed effects estimator is inefficient and a random effects estimator should be used.
2i]), then the HTIV estimator is consistent and more efficient than the fixed effects estimator (Baltagi, 1995).
This creates a hurdle to a complete understanding of the pooled OLS, random effects, and fixed effects estimators and the connection between them.

Full browser ?