fixed-effect model

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).
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Next, Columns (7) through (9) report results from the application of fixed-effect model, Columns (10) through (12) provides a similar report from the estimation of a random-effect model, and, finally, the last three columns do the same job for a random-coefficient model, where coefficient randomness is assumed to apply only to the coefficient of our concern, the level of corruption (cp i).
The fixed-effect model eliminates all cross-county variations, including any unobserved ones.
The fixed-effect model is preferred under such circumstance (Hsiao, 2003).
This result supports the preference to fixed-effect model rather than pool model.
10 the OR from random effect model is shown; otherwise, the OR from the fixed-effect model is shown.
Both tables show the results of (i) the pooled panel model regression, (ii) the fixed-effect model, and (iii) the random- effect model.
In deciding on the use of a random-effect or fixed-effect model for this study, one must note that there are some differences between the two models.
Given the data structure, we employ a one-way fixed-effect model to test the relationship between the insurer's level of diversification and financial performance.
In this study, a fixed-effect model was employed, where it assumed that each experiment in the series was a replication of the others, differing only in response scale and sample sizes.