比如,心理学家和经济学家也许会因为FE和RE的问题“打架”——心理学家可能会说“我们更推荐用随机效应模型(random-effects model)!”,而经济学家可能会说“我们基本都用固定效应模型(fixed-effect model)!”。但实际上,在各自熟悉的知识框架下理解FE和RE,就如同“盲人摸象”,双方可能都只看到了冰山一角。正因为...
When some of the regressors in a panel data model are correlated with the random individual effects,the random effect(RE)estimator becomes inconsistent while the fixed effect(FE)estimator is consistent.Depending on the various degree of such correlation,we can combine the RE estimator and FE ...
When some of the regressors in a panel data model are correlated with the random individual effects, the random effect (RE) estimator becomes inconsistent while the fixed effect (FE) estimator is consistent. Depending on the various degree of such correlation, we can combine the RE estimator ...
Unfortunately, users of mixed effect models often have false preconceptions about what random effects are and how they differ from fixed effects. People hear "random" and think it means something very special about the system being modeled, like fixed effects have to be used when someth...
Given the limited information you've provided, I'd go with fixed-effects, but tell the reader the degree to which specific explanatory variables approach time invariance. One source on this is the Wooldrige econometrics book on cross-sectional and pooled time-series models (sorry but the book...
Subject: st: testing fixed effects versus random effects forclustered data using overiden Hello, I don't have a panel data in the strict sense of the term i.e. I have data on farmers who have several plots/fields. I first perform a ...
We provide a set of conditions sufficient for consistency of a general class of fixed effects instrumental variables (FE-IV) estimators in the context of a correlated random coefficient panel data model, where one ignores the presence of individual-specific slopes. We discuss cases where the assump...
dummy<-summary(rep,"fixed",p.value=FALSE)[,1] ## parameter estimates s_b=dummy[1:max(u_G)] sigsig=dummy[-(1:max(u_G))] Finally, we refit the model letting all parameters vary: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ## STEP 3: REFIT THE MODEL WITHO...
quadratic curvature and can exhibit very different patterns of variability (Fig.1c, Additional file1: Figure S4). To overcome these limitations, RADAR applies a more flexible generalized linear model framework (see the “Material and methods” section) that captures variability through random effects...
The post-processing of the experimental data led to a calculation of a correction coefficient ≈ 1.7 for the average discharge. The same amplification factor has been applied to each individual overtopping volume. Doing that, a time series of experimental values but with scale effects corrected is...