间接变分推断：在收益动态中的应用
Indirect Variational Inference: Applications to Earnings Dynamics
AI总结：
研究收益动态模型中变分推断问题，提出间接变分推断方法，将变分推断作为辅助模型校正偏差，无需计算似然，保留可处理性，灵活变分族结合该方法在模拟和实证应用中能提供可靠估计。
AI中文摘要：
潜变量模型在经济学中至关重要，但常涉及难以处理的积分。变分推断（VI）通过用变分目标替换似然，将积分转化为可处理的可微优化，广泛应用于机器学习。然而，当变分族不够灵活时，恢复真实参数的保证仍然有限，这是VI在经济学中应用的关键障碍。我们首先在收益动态模型中评估VI，表明变分后验的选择至关重要。然后引入间接变分推断（IVI），将VI视为辅助模型并校正变分近似引起的偏差。IVI保留了VI的许多可处理性，因为它不需要计算似然。我们将这些方法应用于允许非线性持久性、非高斯和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。
英文摘要：
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.
