Heterogeneity of consumption responses to income shocks in the presence of nonlinear persistence

IF 9.9 3区 经济学 Q1 ECONOMICS
Manuel Arellano , Richard Blundell , Stéphane Bonhomme , Jack Light
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引用次数: 0

Abstract

In this paper we use the enhanced consumption data in the Panel Survey of Income Dynamics (PSID) from 2005–2017 to explore the transmission of income shocks to consumption. We build on the nonlinear quantile framework introduced in Arellano et al. (2017). Our focus is on the estimation of consumption responses to persistent nonlinear income shocks in the presence of unobserved heterogeneity. To reliably estimate heterogeneous responses in our unbalanced panel, we develop Sequential Monte Carlo computational methods. We find substantial heterogeneity in consumption responses, and uncover latent types of households with different life-cycle consumption behavior. Ordering types according to their average log-consumption, we find that low-consumption types respond more strongly to income shocks at the beginning of the life cycle and when their assets are low, as standard life-cycle theory would predict. In contrast, high-consumption types respond less on average, and in a way that changes little with age or assets. We examine various mechanisms that might explain this heterogeneity.

存在非线性持续性时消费对收入冲击反应的异质性
在本文中,我们使用 2005-2017 年收入动态面板调查(PSID)中的增强型消费数据来探讨收入冲击对消费的传导。我们以 Arellano 等人(2017)中引入的非线性量子框架为基础。我们的重点是在存在未观察到的异质性的情况下,估计消费对持续性非线性收入冲击的反应。为了可靠地估计非平衡面板中的异质性反应,我们开发了序列蒙特卡罗计算方法。我们发现消费反应中存在大量异质性,并发现了具有不同生命周期消费行为的潜在家庭类型。根据平均对数消费排序,我们发现低消费类型在生命周期初期和资产较低时对收入冲击的反应更强烈,这也是标准生命周期理论所预测的。与此相反,高消费类型的平均反应较小,而且随着年龄或资产的变化而变化不大。我们研究了可能解释这种异质性的各种机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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