Approximating grouped fixed effects estimation via fuzzy clustering regression
提出一种计算高效的模糊聚类回归方法，近似Bonhomme和Manresa(2015)的分组固定效应估计，在偏差、分类准确性和计算速度上均有改进。
Summary We propose a new, computationally efficient way to approximate the “grouped fixed effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C‐means objective to regression settings. As the clustering exponent approaches 1, the fuzzy clustering objective converges to the GFE objective, which we recast as a standard generalized method of moments problem. We replicate the empirical results of Bonhomme and Manresa (2015) and show that our estimator delivers almost identical estimates. In simulations, we show that our approach offers improvements in terms of bias, classification accuracy, and computational speed.