Three Essays On The Solution And Estimation Of Dynamic Macroeconomic Models

This thesis consists of three essays in quantitative macroeconomics. In Chapter 1, joint with Leonardo Melosi and Matthias Rottner, we leverage recent developments in machine learning to develop methods to solve and estimate large and complex nonlinear macroeconomic models, e.g. HANK models. Our method relies on neural networks because of their appealing feature that even models with hundreds of state variables can be solved. While likelihood estimation requires the repeated solving of the model, something that is infeasible for highly complex models, we overcome this problem by exploiting the scalability of neural networks. Including the parameters of the model as quasi state variables in the neural network, we solve this extended neural network and apply it directly in the estimation. To show the potential of our approach, we estimate a quantitative HANK model that features nonlinearities on an individual (borrowing limit) and aggregate level (zero lower bound) using simulated data. The model also shows that there is an important economic interaction between the impact of the zero lower bound and the degree of household heterogeneity. Chapter 2 studies the impact of macroprudential limits on mortgage lending in a heterogeneous agent life-cycle model with incomplete markets, long-term mortgage, and default. The model is calibrated to German economy using Household Finance and Consumption Survey data. I consider the effects of four policy instruments: loan-to-value limit, debt-toincome limit, payment-to-income limit, and maximum maturity. I find that their effect on 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 largest positive welfare effect. Chapter 3 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.

This dissertation consists of two chapters, both of which approach macroeconomic issues using simulation-based methods. Aside from the fact that each chapter contributes to its narrowly scoped field, the two chapters demonstrate an implementation of simulation-based estimation techniques and identification strategies to examine dynamic properties of unobserved economic shocks. The main objective of two chapters is to understand the properties of shock process, which in turn provides better macroeconomic implications. The first chapter structurally estimates idiosyncratic labor income risks over the life-cycle to obtain implications for a redistribution policy, namely tax and transfer systems. Since a redistribution policy provides a partial insurance to those exposed to income risks, understanding the underlying life-time labor income risks that households face is central to designing better institutional arrangements. The chapter constructs a human capital life-cycle model and structurally estimates the underlying source of labor income risks across age. We find that the estimated shock process is significantly age-dependent even after controlling for the endogenous responses to the exogenous shocks. In particular, young workers encounter a highly persistent (almost unit-root) but relatively small volatility of permanent shocks, while older workers encounter a less persistent but higher volatility of permanent shocks. In addition, we demonstrate that under the age-dependent shock process the self-insurance ability of young workers is 20% lower than that of middle-aged workers. Finally, we find that more benefits, either through a tax exemption or subsidies, to young workers drastically improve aggregate production, welfare, and income inequality. In the second chapter, we structurally estimate the dynamic properties of elasticity of substitution between capital and labor to resolve well-known puzzles in labor market dynamics: Dunlop-Tarshis phenomenon, the labor productivity puzzle, the labor share puzzle including its oveshooting response to productivity shocks, and the hours-productivity puzzle. We propose an aggregate production function that potentially takes a different shape in the short run (SR) from the long run (LR). Specifically, we allow for cyclical fluctuations of the short-run elasticity of substitution between capital and labor, [sigma][subscript t], while keeping the Cobb-Douglas shape in the long run. We find that productivity shocks are on average biased toward labor (i.e. [sigma][subscript t]

Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.