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This dissertation consists of three essays on firm finances and macroeconomics. In Chapter 1, I empirically investigate the relationship between firms' financial positions and asset tangibility by drawing on a CRSP/Compustat merged dataset of US public firms from 1987 to 2016. Intangible capital has grown in importance as the US economy has evolved towards service-based and technology-based industries with a decline in the physical capital share. Intangible capital spending is a type of capital expenditure that is not negligible compared to physical capital investment. The key finding of my empirical exercise is that industries and firms with lower average asset tangibility have lower average debt-to-sales ratios and higher average values of distance-to-default both in the long run and short run. Asset tangibility is a proxy for the recovery rate of capital since intangible capital is considered less valuable collateral, so the empirical evidence suggests that the recovery rate of capital is related to borrowing and default. Chapter 2 structurally estimates the recovery rate of capital, which is difficult to observe in the data, and quantitatively analyzes the aggregate implications of the empirical findings in the previous chapter. The recovery rate of capital determines lenders' credit supply and affects the demand and total credit amounts in equilibrium. Recent rising intangibles in the US may reduce recovery. I build a canonical quantitative general equilibrium heterogeneous firm model with risky debt, capital accumulation, and default. I estimate the model parameters by matching the covariance matrix of profit, investment, and debt, the average spread, and the average default rate in my data sample. The simulated method of moments (SMM) estimate of the recovery rate is 74% when targeting moments constructed with only physical capital. The counterfactuals reveal that declines in the recovery rate reduce aggregate output, credit, and welfare by constraining capital accumulation. Tackling intangibles by a broader notion of capital, I estimate a recovery rate of 46% with the same model structure, implying that rising intangibles could cause nontrivial output and welfare losses due to financial frictions. Chapter 3 examines the causal effect of immigration on local entrepreneurship in US counties. I use the immigration shock constructed in Burchardi et al. (2020) as an instrumental variable to predict the total number of migrants flowing into each US county from 1990 to 2010. I use the entrepreneurship indices from the Startup Cartography Project (Andrews et al., 2020) to measure the quantity and quality of US start-ups at the county-level. First, I find a strong and significant causal impact of immigration on the number of new business registrants per person. Second, I find a significant causal impact of immigration on the expected number of start-ups with growth per person. I also show that the influx of immigrants can increase the local average wage per capita. To interpret these empirical findings, I build a model of entrepreneurship which implies that if immigration shifts the distribution of entrepreneurial acumen to the right, it increases the wage rate, the fraction of entrepreneurs, and the mean quality of entrepreneurs. These results suggest that immigration is an essential driver of economic dynamism via entrepreneurship.

In chapter one, I examine the predictive content of subjective return expectations derived from price targets issued by equity analysts. Equity price targets are an ubiquitous feature of the financial information landscape, but it is not clear how informative they actually are. In this chapter, I show that the cross-section of price-target implied subjective return expectations contains rich informational content for forecasting returns. In-sample, I find that expected returns correlate strongly with average cross-sectional returns to a large panel of portfolios formed on the basis of observable firm characteristics. In out-of-sample exercises, forecasting models using subjective expectations are shown to offer more accurate predictions for portfolio returns than several other commonly employed, cross-sectional predictors, including the book-to-market and dividend-price ratios, momentum, and forward-looking cash-flow measures. Furthermore, these differences are shown to be economically relevant, with conditional portfolios formed on the basis of subjective expectations offering substantially improved risk-adjusted returns compared to many of the other predictors considered. The relative informational content, as well as the production by analysts, of subjective return expectations is found, however, to peak during recessions, with negligible predictive advantage discernible in expansions. In chapter two, my coauthors (Adam Rej, with CFM; David Thesmar, with MIT, CEPR, and NBER) and I empirically analyze a large panel of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns. In chapter three, my coauthor (Ken Teoh, with Columbia) and I construct a novel text-based measure of firm-level attention to macroeconomic conditions and document that stocks associated with higher macroeconomic attention earn lower returns. Moving from the bottom decile to top decile of macroeconomic attention decreases a stock’s average return by 11.6\% per year. We propose a risk-based explanation in which stocks with higher macroeconomic attention contribute less idiosyncratic cash flow risk to the investor’s portfolio, hence earn lower expected returns. Decomposing the unexpected returns of macroeconomic attention-sorted portfolios into cash flow and discount rate news, we find that portfolios with higher macroeconomic attention stocks have lower cash flow risk.

Since the federal funds rate reached the zero lower bound in late 2008, economists have been struggling to adapt their models to a long-term zero rate. Wu and Xia built upon previous research by Fischer Black to create a model for how the federal funds rate behaves during the ZLB period. In their model, the rate actually dips into the negative digits, which the actual federal funds rate does not do. The logic behind the model is that a negative shadow rate is a much better indicator of true economic conditions while the current zero rate merely masks the actual economic reality. It is also easier to use the shadow rate for trend analysis purposes, since the shadow rate is flexible and changes while the federal funds rate remains artificially fixed at zero. Thus, this paper seeks to provide a comparison between the Shadow Rate, as defined by Wu and Xia, and how three key banking variables (leverage, profitability, and non-performing loans to total loans) react in response to the shadow rate, along with three control variables: real GDP growth, inflation, and the current account to GDP ratio. Regression will also be used to determine how three key borrower variables (S&P 500 Index, Credibility Consumer Distress Index, and the ratio of nonfinancial corporate business debt securities to total assets) interact with the shadow rate and the three control variables previously mentioned.

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The purpose of this study is to explore the influence on urban employment patterns of changes in demand for commodities by foreign and domestic consumers. Foreign induced changes in commodity demand are reflected in this study by assumed changes in exports of selected comma-· dity groupss ranging from relatively unprocessed groups such as grain, to sophisticated groups such as electrical products and chemicals. The domestic sources of commodity demand change considered in this study are various components of current expenditure by the federal government on health, education and defense programs, as well as on total government expenditure. The influence of these sources of final demand change is traced to the employed populations of Montreal and Toronto metropolitan areas, and the component districts of these urban areas. An important concern is with whether or not some shocks tend to alleviate or accentuate existing unemployment rate disparities between the central city and fringe of Montreal and Toronto. A national input-output system, together with an appended employment allocation matrix is utilized to estimate the urban employment impacts. The area impacts differ because, on the one hand, employment in some industries is affected more than in others, depending on the particular source of final demand change assumed, and on the other hand the proportion of an area's employed population affiliated with a given industry tends to differ from that of other areas. Before implementing the model the latter proposition~ concerning inter-area differences in industrial affiliation pattern of the employed) is supported by theoretical reasoning and empirical analysis. Theoretically different industries have different locational preferences in an urban area, as a result of factors related to technology, cost of production, and market access. Combined with the theoretical assumption concerning minimization of cost and/or distance of travel to work, area differences in the proportion of workers affiliated with a given industry is implied. This hypothesis is not rejected by analysis of variance experiments based on the pattern of male and female employed populations residing in districts of Montreal and Toronto. Adjusted census statistics on the employed population are used in these experiments the adjustment being required in order to make the urban portion of the model consistent with the 1961 input-output system. Implementation of the model reveals that the metropolitan areas of Montreal and Toronto are influenced to similar degrees by the assumed changes in various components of final demand, but that certain sub-metro areas were affected more than others. There is a tendency for suburban and wealthier areas to be affected more than central and less affluent districts though there are important exceptions. Some components of final demand change tend to accentuate existing intra-urban unemployment disparities. It is finally shown how the area impact disaggregated by subpopulation can be used to identify structural factors responsible for inter-area differences in the total impact. The disaggregated impacts also reveal qualitative, or distributional aspects of the aggregate impacts and thus may be of interest to urban planners. It is possibles for example, to check if female or male employees, affiliated with a lower paying industry group, and resident in a relatively poor district of the city, is influenced more than average by a particular type of final demand change. The limitations and possible extensions are finally reviewed. One limitation involves the assumption that given the industry, subpopulations of the employed are discharged at similar rates when there is a fall in product demand. The theory treating labour as a quasifixed factor implies that the lower grades of labour would be discharged at higher rates than the higher grades. Any bias due to the omission of this effect would reinforce the results related to intra-urban unemployment rate disparities, however. Future research suggested by this study include incorporation of the discriminatory discharge effect into the model and further disaggregation of the work force of industries according to occupation or income group. / Doctor of Philosophy (PhD)

<p>In this dissertation I present three chapters all related by their focus on issues of money and finance and the methodological treatment, the New Monetarist methodology, each considering different aspects of the international monetary-financial system. Williamson and Wright (2010) describe the New Monetarist methodology in detail, including enumerating several key principles, which put emphasis on microfoundations, especially of the frictions in the exchange process and financial intermediation arising from the environment, which are essential to analyses of macro and monetary economics. </p> <p><br></p> <p>The first chapter is entitled ``Optimal Monetary Policy and the Welfare Cost of Inflation of a Currency Union.'' It seeks to answer the question: How do retail trade frictions interact with immigration choices, and how does this interaction affect monetary policy in a currency union? This chapter studies the welfare cost of inflation and optimal monetary policy of a currency union between two countries using a search-theoretic framework with endogenous composition of buyers and sellers. The model includes three features of a currency union that are key to welfare and policy analysis: heterogeneous market structure, imperfect market integration, and immigration policy. The model yields optimal monetary policy that deviates from the Friedman rule, with the magnitude of inflation rate and welfare cost determined by different policy regimes. The Friedman rule is suboptimal, because a matching congestion externality in the labor market arises from the endogenous composition of buyers and sellers. Higher labor mobility reduces the cost of inflation by alleviating congestion, regardless of buyers' bargaining power. Market integration may also reduce congestion, lowering the cost of inflation, but only when sellers are relatively scarce.</p> <p><br></p> <p>The second chapter is entitled ``Liquidity, Collective Moral Hazard, and Government Bailouts''. It tries to answer the question: How does financial intermediaries risk taking respond to monetary/fiscal policy including bailout policy?</p> <p>This chapter develops a general equilibrium model of assets market integrating a theory of liquidity risk in a New Monetarist framework. Collective moral hazard arises from the interaction between banks' maturity transformation and government intervention. With the anticipation and implementation of government bailouts during a crisis, collective moral hazard creates current and deferred social costs. However,  under the ``correct" monetary policy, the costs are justified by the improvement of liquidity condition as a result of higher provision of public and private liquidity.</p> <p><br></p> <p>The third chapter is entitled  ``On Cross-Border Payments and the Industrial Organization of Correspondent Banking.''  It tries to answer the question: How does an understanding of the market structure of international banking affect recent suggestions for the improvement of cross-border payments?</p> <p>Despite advances in domestic payments arrangements in recent times, cross-border payments remain expensive and slow. This paper builds a model of bank-intermediated cross-border payments, identifying market power inherent to correspondent banking relationships as the key friction reducing efficiency. After developing a simple model of correspondent banking, we consider two policy experiments: the introduction by one country of an internationally held central bank digital currency (CBDC), and development of an internationally interoperable settlement system. Both policies can at least attenuate the inefficiencies in cross-border payments, but neither is automatically a complete solution nor are they without difficulties. For an international CBDC, we identify a political economy barrier: banks of the country potentially introducing the CBDC disproportionately suffer losses while benefits tend to accrue to foreign depositors, so a central bank concerned with its domestic banks' profitability may be unlikely to take such an action. Interoperability does not face the same barrier, as benefits accrue more symmetrically, but we argue that technical and other issues inherent to interoperability may be difficult to overcome.</p> <p><br></p>

Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&amp;P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&amp;P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. / Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&amp;P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&amp;P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

The assertion that financial flows are the primary drivers of exchange rates may be considered as financial markets become increasingly large and sophisticated. However, the Post-Keynesian critique leaves little room for the real economy to impact exchange rates. This paper aims to extend the Mundell-Fleming model to address the Post-Keynesian critique of mainstream models, by incorporating wealth effects, expectations, and Taylor-rule interest targeting. Discussion of significant financial events affecting the USDJPY exchange rate finds that wealth effects are significant considerations, and that the real economy cannot be discounted completely. Empirical results find that the real interest rate is a significant factor in exchange rate determination, tying into the discussion over the relationship between savings and consumption.