In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or ...

To implement the fixed effects model, we use the PanelOLS method, and set the parameter `entity_effects` to be True. mod = PanelOLS(data.clscrap, exog) re_res = mod.fit() print(re_res) The results are ...

The more general MIXED procedure fits mixed linear models containing both fixed and random effects. The MIXED procedure provides easy accessibility to a variety of mixed models useful in many common ...

The meta-analytic random effects model assumes that the variability in effect size estimates drawn from a set of studies can be decomposed into two parts: heterogeneity due to random population ...

Facebook today announced an AI model trained on a billion images that ostensibly achieves state-of-the-art results on a range of computer vision benchmarks.

Our paper discusses how random coefficient models (RCMs) may generate new insights about firm heterogeneity and its effects on performance in empirical settings in strategy. RCMs allow testing for and ...