Browsing by Author "Lanteri, Andrea"

This dissertation has three independent essays. The first essay “Macro Shocks and Firm Dynamics with Oligopolistic Financial Intermediaries” studies the macroeconomic effects of oligopolistic competition in the financial intermediation sector. Motivated by a recent in- crease in the concentration of the US banking industry and several empirical facts about the effects of bank competition on firm dynamics, I develop a novel dynamic general equilib- rium model with oligopolistic banks and heterogeneous firms. Strategic interactions among oligopolistic banks generate endogenous financial frictions that shape firm investment and financing dynamics, affecting aggregate productivity. I introduce two sources of aggregate shocks: a sudden increase in the aggregate firms’ default probability and a “big bank” fail- ure (e.g., Lehman Brothers in 2008). When the probability of firms’ default increases, banks exploit their marker power to extract higher markups and credit spreads increase. This mechanism allows banks to compensate for the larger losses due to defaults, but it leads to a larger decline in real activity. When the economy is also hit by a “Lehman shock”, the model accounts both qualitatively and quantitatively for key macroeconomic and financial features of the Great Recession. In an extension, I also study banks’ market power in a model with idiosyncratic firms’ TFP shocks and endogenous default. Higher concentration in the banking sector reduces the frequency of firms’ default but makes the economy less productive. The second essay “Capital and Labor Taxes with Costly State Contingency” studies op- timal capital and labor taxes in a model where (i) the government makes noncontingent announcements about future policies, and (ii) ex-post statecontingent deviations from these announcements are costly. We find that costly state contingency has important implications for the response of taxes and allocations to government spending shocks. Different from previous results based on freely statecontingent taxes, the volatility of capital taxes is low and labor taxes play a fundamental role in accommodating fiscal shocks, increasing persis- tently when government spending increases. Moreover, private consumption becomes highly responsive to government spending. We also characterize optimal fiscal announcements. Under Full Commitment, announcements are unbiased, i.e., they coincide with expected policies. When governments lack commitment, instead, fiscal announcements play a strate- gic role and governments use them to constrain future policies; as a result, optimal fiscal announcements are biased, but may sustain similar outcomes to the ones associated with Full Commitment. The third essay “Machine Learning Projection Method for Macro-Finance Models” develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. The method uses an artificial neural network (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm (PEA). Because this method can process the entire information set at once, it is easily scalable to handle models with large state spaces that are highly collinear. The paper demonstrates these computational gains in two applications. First, the paper extends the optimal government debt problem studied by [FMOS19a] to ten maturities and finds that, when borrowing and lending constraints are tight, the optimal policy prescribes an active role for the medium-term maturities. Second, the paper reassesses the resolution of the international business cycle puzzles in [KP02]. This paper shows that extending their two-country framework to three countries, namely US Europe and China, can change the risk-sharing properties of the economy significantly.

This dissertation is comprised of three chapters in macroeconomics, entrepreneurship, and heterogeneous agent models. In the first chapter, I answer two main questions --- What are the empirical facts regarding entrepreneurial investment choices, and to what extent do the investment choices of entrepreneurs help to explain top wealth inequality. To that end, I document some novel facts about entrepreneurial investment dynamics. I show that these facts are suggestive that capital illiquidity are an important friction with regards to entrepreneurial investment choices. To quantify these frictions, I construct a new general-equilibrium heterogeneous agents model of entrepreneurship that features illiquid investments. I calibrate this model to identifying features of the data, and find a large role for illiquidity. I also find that the investment and savings choices of entrepreneurs help explain a substantial fraction of wealth inequality. Counterfactual analysis shows that the illiquidity friction generates substantial welfare and productivity losses by allocating wealth away from high productivity entrepreneurs to low productivity entrepreneurs, which simultaneously leads to lower wealth inequality. As such, I find that a policy of partial insurance against illiquidity risks can help ameliorate these losses, and simultaneously increases wealth inequality. In my second chapter, I present new evidence regarding the effect of uncertainty shocks on firm startup and exit rates. I document that uncertainty shocks are strongly and negatively correlated with firm startup rates, but essentially uncorrelated with exit rates. I show how my model of illiquid entrepreneurial investments can help explain these facts, and argue that capturing the extensive margin of adjustment to uncertainty shocks is important in amplifying and propagating the effects of uncertainty shocks. Finally, in my last chapter, I present a new computational algorithm to compute distributions in heterogeneous agent models. I show how this algorithm improves on current methods by reducing the amount of computational memory and time required, and provide a simple and intuitive explanation as to how this algorithm improves on the textbook method.

This dissertation is comprised of three chapters in macroeconomics, entrepreneurship, and heterogeneous agent models. In the first chapter, I answer two main questions --- What are the empirical facts regarding entrepreneurial investment choices, and to what extent do the investment choices of entrepreneurs help to explain top wealth inequality. To that end, I document some novel facts about entrepreneurial investment dynamics. I show that these facts are suggestive that capital illiquidity are an important friction with regards to entrepreneurial investment choices. To quantify these frictions, I construct a new general-equilibrium heterogeneous agents model of entrepreneurship that features illiquid investments. I calibrate this model to identifying features of the data, and find a large role for illiquidity. I also find that the investment and savings choices of entrepreneurs help explain a substantial fraction of wealth inequality. Counterfactual analysis shows that the illiquidity friction generates substantial welfare and productivity losses by allocating wealth away from high productivity entrepreneurs to low productivity entrepreneurs, which simultaneously leads to lower wealth inequality. As such, I find that a policy of partial insurance against illiquidity risks can help ameliorate these losses, and simultaneously increases wealth inequality. In my second chapter, I present new evidence regarding the effect of uncertainty shocks on firm startup and exit rates. I document that uncertainty shocks are strongly and negatively correlated with firm startup rates, but essentially uncorrelated with exit rates. I show how my model of illiquid entrepreneurial investments can help explain these facts, and argue that capturing the extensive margin of adjustment to uncertainty shocks is important in amplifying and propagating the effects of uncertainty shocks. Finally, in my last chapter, I present a new computational algorithm to compute distributions in heterogeneous agent models. I show how this algorithm improves on current methods by reducing the amount of computational memory and time required, and provide a simple and intuitive explanation as to how this algorithm improves on the textbook method.