random effects model

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random effects model

A statistical model that may be used in meta-analysis, in which both within-study sampling error (variance) and between-studies variation are included in assessing the uncertainty or confidence interval of the results of the meta-analysis.
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This paper use Hausman test paper in the domestic and foreign general for the use of the model, test results show that the covariance matrix is not positive, Hausman test results cannot be used as evaluation criteria, this paper also shows the results of the empirical report of fixed and random effect model analysis results, in addition to the difference of K1 coefficient is large, the results of two models the difference is not great, the fixed effect model and random effect model has certain adaptability.
This model is quite analogous to the random effect model.
The above Table 2, shows the results of the random effect model of panel data regression of dividend payout ratio and profitability.
001), so the random effect model was employed to estimate the pooled prevalence of 44% (95% CI = 36%, 52%).
The P value hows that overall random effect model is good fit, but the value of Durbin-Watson test not better as compare to than fixed effect model.
For model 3, however, using random effect model seems to be a more appropriate choice.
When calculating the overall effect, the study showing a weight in random effect model below 5% was regarded as out-layer and not applied to analysis.
The random effect model was used to determine pooled effect estimates, since this model is more conservative.
Table 4: The Random Effect Model Dependent Variable: LN_DEMO Variable Coefficient Std.