间接变分推断：在收入动态中的应用

摘要

潜变量模型在经济学中至关重要，但通常涉及难以处理的积分问题。变分推断（VI）在机器学习中广泛应用，它通过用变分目标替代似然函数，将难以处理的积分转化为易于处理且可微的优化问题。然而，当变分族灵活性不足时，恢复真实参数的保证仍然有限——这是变分推断在经济学中应用的一个关键障碍。我们首先在收入动态模型中评估变分推断，并表明变分后验的选择至关重要。然后我们引入间接变分推断（IVI），它将变分推断视为辅助模型，并纠正变分近似引起的偏差。IVI保留了变分推断的许多易处理性，因为它不需要计算似然函数。我们将这些方法应用于允许非线性持续性、非高斯且序列相关的暂时冲击以及潜在异质性的模型。在模拟和实证应用中，灵活的变分族与IVI相结合可提供可靠的估计。

English

Latent-variable models are central to economics but often entail intractable integration. Variational inference (VI), widely used in machine learning, turns this integration into tractable, differentiable optimization by replacing the likelihood with a variational objective. However, guarantees of recovering the true parameters remain limited when the variational family is insufficiently flexible -- a key obstacle to the adoption of VI in economics. We first evaluate VI in models of earnings dynamics and show that the choice of variational posterior is crucial. We then introduce indirect variational inference (IVI), which treats VI as an auxiliary model and corrects the bias induced by the variational approximation. IVI retains much of VI's tractability because it does not require computing the likelihood. We apply these methods to models allowing for nonlinear persistence, non-Gaussian and serially correlated transitory shocks, and latent heterogeneity. Across simulated and empirical applications, flexible variational families combined with IVI deliver reliable estimates.

间接变分推断：在收入动态中的应用

Indirect Variational Inference: Applications to Earnings Dynamics

摘要

Support