We estimate the distribution of marginal propensities to consume (MPCs) using a new approach based on the fuzzy C-means algorithm (Dunn 1973; Bezdek 1981). The algorithm generalizes the K-means methodology of Bonhomme and Manresa (2015) to allow for uncertain group assignment and to recover...

We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full...

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...

We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full...

We estimate the distribution of marginal propensities to consume (MPCs) using a new approach based on the fuzzy C-means algorithm (Dunn 1973; Bezdek 1981). The algorithm generalizes the K-means methodology of Bonhomme and Manresa (2015) to allow for uncertain group assignment and to recover...

We estimate the distribution of marginal propensities to consume (MPCs) using a novel clustering approach that generalizes the fuzzy C-means algorithm to regression settings. We apply the estimator to the 2008 stimulus payments, exploiting the randomized timing of disbursements, and find...

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...

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...

Identification based on higher moments has drawn increasing theoretical attention and been widely adopted in empirical practice in macroeconometrics in the last two decades. This article reviews two parallel strands of the literature: identification strategies based on heteroskedasticity and...