Job Market Paper

Abstract:  This paper proposes a novel solution framework for the class of dynamic macroeconomic models with a continuum of heterogeneous agents and aggregate uncertainty. In these models, an agent's state variables include her individual state vector and a distribution function representing all agents' states, an infinite-dimensional object. Unlike the prevalent benchmark method that approximates the distribution function with a high-dimensional vector of simulated agents, this paper suggests the formulation of the policy function as an operator that maps between function spaces. The operator is parameterized by a cutting-edge neural network architecture known as the neural operator. This proposed framework offers significant computational advantages due to its three defining properties: discretization-invariance, permutation-invariance, and aggregation-sharing. The effectiveness of this approach is demonstrated by solving the Bewley-Huggett-Aiyagari model with aggregate uncertainty, a benchmark in computational economics literature. The proposed framework not only demonstrates computational efficiency as it manages tens of thousands of agents during simulations to precisely approximate the distribution function but also showcases its superior performance, achieving solutions with less than a one percent relative error in a shorter computational time compared to the benchmark method.

Working Papers

Abstract: The literature on child development traditionally emphasizes maternal time use, female labor supply, and their impact on child outcomes. This study uncovers two key findings: First, fathers exhibit significantly greater variation in time spent with their children compared to mothers. Second, there is a strong positive relationship between fathers’ labor market participation and their involvement in childcare—a trend not observed among mothers. This suggests that the long-assumed trade-off between labor market participation and childcare does not apply to fathers in the same way it does to mothers. To quantify the impact of this paternal time investment heterogeneity on child development inequality, this study estimates production functions for cognitive and health development in children aged 1-18, using data from both fathers and mothers in the PSID time diary and child development supplement. The analysis, based on a nonlinear latent factor model with gender-specific labor demand shocks as instruments, reveals a striking result: Eliminating paternal time investment heterogeneity reduces the variance in child cognition by 22 percent and child health variance by an impressive 49 percent, particularly among children aged 12-18. These findings underscore the significant role of fathers in child development and contribute to the literature on intergenerational mobility by investigating factors based on parental actions, rather than parental identity, with a special emphasis on the heterogeneity of fathers’ time allocation.

Abstract: This study delves into the numerical resolution of a critical economic model category, Markov Equilibrium Games, employing a multi-agent deep reinforcement learning algorithm for strategy optimization. Specifically, it focuses on a duopoly context, resembling a Stackelberg game, where two firms engage in sequential decision-making each period. Characterized as model-free learners, these firms initially lack economic theoretical knowledge and develop optimal decision-making strategies solely through mutual interactions in simulations. Their policy functions are defined using neural networks, and training is executed via the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm, a concept from deep reinforcement learning. The study aims to investigate if, under these conditions, the economy can attain an equilibrium where each firm's behavior is optimal. This exploration is set in a linear-quadratic framework, allowing for analytical derivation of the firms’ optimal policy functions. The experimental findings indicate a nuanced outcome: the economy occasionally aligns with the analytical equilibrium, but diverges at times. These outcomes provide valuable insights for economists in refining model formulation and applying model-free numerical solutions in economic analysis.


Abstract: This report replicates and examines Bauer et al.'s (2021) paper on monetary policy transmission to financial markets. The paper introduces novel measures of monetary policy uncertainty and analyses its drivers. It also investigates the impact of uncertainty changes on interest rates and financial asset prices. We assess reproducibility, consolidate market uncertainty measures using PCA and Factor Analysis, and rigorously test the reduction of uncertainty after Federal Market Open Committee (FOMC) announcements. Our findings support the paper's claim of reduced uncertainty on meeting days. Additionally, we explore the implications of the uncertainty channel on various financial assets, such as Gold, the Swiss Franc, European stock indexes, and Bitcoin.