## Working Papers

### Estimating Nonlinear Heterogeneous Agent Models with Neural Networks

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate uncertainty. Using simulated data, we show that the proposed method accurately estimates the parameters of a nonlinear Heterogeneous Agent New Keynesian (HANK) model with a zero lower bound (ZLB) constraint. We further apply our method to estimate this HANK model using U.S. data. In the estimated model, the interaction between the ZLB constraint and idiosyncratic income risks emerges as a key source of aggregate output volatility.
Presented at
- The Society for Nonlinear Dynamics and Econometrics (SNDE) Symposium (March 2022)
- The Society for Economic Measurement (SEM) Annual Conference (August 2022)
- The European Economic Association (EEA) and European meeting of the Econometric Society (ESEM) Conference (August 2022)
- Conference on Non-traditional Data, Machine Learning and Natural Language Processing in Macroeconomics at Sveriges Riksbank (October 2022)
- Midwest Macro Meeting (November 2022)
- ASSA (January 2023)
- CEF (July 2023)
- NBER Summer Institute (July 2023)
- DSE (August 2023)
- SED (June 2024)
- ECB (October 2024)
- Goethe University Frankfurt, Numerical Methods in Macroeconomic (October 2024)

## Work in Progress

### Nonlinear Phillips Curve and Inflation Risk

How does a nonlinear Phillips curve affect inflation risk? Using a strategic surveys approach and micro price data, we establish that the price setting behaviour of firms depends nonlinearly on the inflation environment. In a high inflation environment, the share of firms that adjust their prices in response to expected inflation increases. We rationalize these dynamics using a quantitative macroeconomic model with a nonlinear Phillips curve. The model features a tractable heterogeneous firm setup with endogenous varying degrees of price flexibility. Solving the model with a machine learning approach, we demonstrate that, in this setting, contractionary supply shocks lead to higher inflation, which provides a new motive for the monetary policy to act preemptively.
Presented at
- SEM (July 2023)

### Sequence-Space Jacobian meets Deep Learning: Exploiting the Random Walk for HANK

This paper introduces an innovative approach integrating deep learning techniques with sequence-space Jacobian methods to enhance Bayesian estimation in heterogeneous agent New-Keynesian (HANK) models. By employing a deep neural network as a surrogate for the posterior, we aim to accelerate the Bayesian estimation process significantly. This network is trained on a dataset comprising true model likelihoods generated through a parallel Metropolis-Hastings algorithm. Our method uniquely leverages all generated draws, including both accepted and rejected ones, thereby ensuring a thorough exploration of the parameter space. This strategy not only alleviates the computational burden traditionally associated with Bayesian estimation but also demonstrates remarkable efficiency in estimating parameters that necessitate the resolution of the model's steady state and the recalculation of Jacobians. Our work stands at the frontier of integrating advanced computational techniques with economic modeling, promising substantial advancements in estimating and understanding complex heterogeneous agent models.
Presented at
- CEF 2024 (June 2024)
- SEM 2024 (August 2024)

### Backpropagating Through Heterogeneous Agent Models

This paper explores applications of the backpropagation algorithm on heterogeneous agent models. In addition, I clarify the connection between deep learning and dynamic structural models by showing how a standard value function iteration algorithm can be viewed as a recurrent convolutional neural network. As a result, many advances in the field of machine learning can carry over to economics. This in turn makes the solution and estimation of more complex models feasible.
### Limits on Mortgage Lending

This paper aims to study the impact of macroprudential limits on mortgage lending in a heterogeneous agent life-cycle model with incomplete markets, long-term mortgages, and defaults. Using data from the Household Finance and Consumption Survey, the model is calibrated for the German economy. I consider the effects of four policy instruments: loan-to-value limit, debt-to-income limit, payment-to-income limit, and maximum maturity. I find that their effect on the homeownership rate is fairly modest. Only the loan-to-value limit significantly reduces the homeownership rate among young households. At the same time, it has the most significant positive welfare effect
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