Doctor of Philosophy / Department of Economics / Lance Bachmeier / Steven Cassou / This dissertation consists of three essays in macroeconomic econometrics. The first essay investigates industry level production functions. Part of the interest in doing this is to contribute to the ongoing improvements in dynamic macroeconomic models which are increasingly disaggregating economies into industrial sectors. This paper provides useful production function parameter values for this endeavour. In addition, the paper shows that there are differences across industry level production functions, so model disaggregation cannot rely on a generic scaled down aggregate production function. Futhermore, evidence of these differences is provided in several ways. First, it is shown that some, but not all, industry level production functions exhibit constant returns to scale. Second, conducted pairwise tests show whether government capital production elasticities are the same for different pairs of industries. In the majority of these tests, the null hypothesis was rejected. In the second essay, the relevance of wage rigidities for understanding the effect of oil price shocks on output and inflation is examined. The theoretical framework of Blanchard and Gali (2007) is adopted and modified in two important ways. First, an empirically estimated wage adjustment cost function is incorporated following work by Kim and Ruge-Murcia (2009). Second, a realistic monetary policy function is incorporated into the model to be consistent with the current macroeconomic literature. The paper provides evidence that the degree of wage stickiness has little effect on the oil price-macroeconomy relationship. We find that the only way to generate large changes in the variances of output and inflation is to increase the wage adjustment cost by an extreme amount. The third essay assesses the statistical adequacy of the Cobb-Douglas aggregate production function with public capital as an input. The paper tests the statistical adequacy of the models proposed by Aschauer (1989a) and Tatom (1991) and finds that both models are misspecified. Furthermore, the paper finds that Tatom's model suffers from the same criticism he levels against Aschauer's model, non-stationarity in the data series used to estimate the model. Using Aschauer's framework, a properly specified model is found that models both deterministic heterogeneity and serial autocoreelation. Model results find that public capital is positive and significant. The results are in contrast to a large body of literature that discredits Aschauer's findings claiming his model is incorrect. Finally, an additional specification of the model using the student's t linear regression model is explored to capture potential heteroskedasticity.

M.Comm. / Economists usually distinguish between five macroeconomic objectives, namely, high and sustainable economic growth, full employment, price stability, balance of payments stability and the equitable distribution of income. This research deals with the economics of organised crime. It aims to examine the relationship between organised crime and the five macroeconomic objectives. To prove that organised crime has an impact on macroeconomic stability, it is necessary to show that it involves large sums of money relative to overall economic activity. Crime has always been mentioned as a factor that has an impact on the economic growth of a country, but the extent to which crime constrains growth and by what mechanisms it limits growth and development is unknown. This can be attributed to the underground nature of most organised criminal activities, such as money laundering. Very little research has been done on the costs and the extent of organised crime on the macroeconomy of South Africa. In attempting to quantify the costs of crime relative to the macroeconomy of South Africa, this research identifies various organised criminal activities. It examines the extent of the costs and the threats they pose to the macroeconomic stability of South Africa. This research shows that the political transformation and the resultant globalisation of South Africa during the early 1990s provided an ideal opportunity for organised crime structures to expand. It also shows that organised crime imposes direct and indirect costs on households and businesses, and therefore on the economy of South Africa as a whole. Organised crime diverts funds that could otherwise be invested in productive capacity, it discourages foreign investment and induces the government to spend money on law enforcement, crime prevention and the administration of justice, instead of spending it on the creation of additional employment opportunities. Tax revenue is also lost to money laundering. The abuse of the informal economy by money launderers has an impact on growth. Crime has prevented the growth of the tourist industry to its full potential given the country’s reputation of violence. A loss of skilled personnel who left the country has also been experienced, citing crime as a reason to immigrate.

M.Com. / Please refer to full text to view abstract

If lending contracts in an economy take the form of unsecured, non-state-contingent debt, a recession will often be associated with an increase in defaults and a reduction in the supply of credit, which amplifies the contraction. Within this context, I consider the case when the length of an unfolding recession is not immediately obvious; instead, it takes people time to learn about a persistent recession. I apply this scenario to models where the unsecured credit is represented by mortgage loans extended to households and by sovereign debt extended to an emerging economy. I find that in both cases, accounting for uncertainty and learning has a potential to improve the empirical performance of the models. Chapter 1 explores the U.S. housing market where house prices show a lot of inertia. I develop a general-equilibrium model with the market for housing and mortgages and introduce uncertainty regarding the persistence of business cycles. I show that uncertainty allows the model to better account for the sluggish dynamics of the housing market. In Chapter 2, I use key U.S. macroeconomic data to empirically estimate the structural model developed in Chapter 1. In order to compare the performance of the models with and without uncertainty, I use likelihood-based estimation methods. The model with uncertainty proves to be better capable of mimicking the long-lasting changes in house prices and other observable variables. Chapter 3 contains a theoretical model of a small open emerging economy that looks to refinance its sovereign debt during an unfolding recession of uncertain length. A long recession implies a higher chance of default in the future; a short recession means quick recovery and solvency. Uncertainty about the unfolding scenario adds price risk to long-term bonds and makes them costly to the borrower. Investors' preferences shift towards short-term bonds which mature before a lot of the uncertainty is resolved and before credit events are likely to happen. Such uncertainty helps explain the empirical fact that emerging economies tend to borrow short term during economic downturns.

The Dynamic Stochastic General Equilibrium (DSGE) model, which is based on the New Consensus Macroeconomics (NCM) theoretical framework, has become the workhorse of macroeconomic analysis in academia, research institutes and monetary authorities since the 1980s. The dominating popularity of the DSGE type of models can be witnessed by their extensive use by central banks, such as the Bank of England (BoE), the European Central Bank (ECB), the Federal Reserve (FED) and other central banks. One of the most important and attractive advantages of the DSGE model is its compatibility with a variety of micro- and macro- economic foundations, including short-run nominal rigidities in the goods and services markets, heterogeneities in production, monetary policy and a rich set of exogenous shocks; not that there are no problems with these aspects of the DSGE model as discussed in this thesis. Although a lot of efforts have been made in DSGE modelling in industrialized economies, literature of DSGE modelling in emerging market economies is still at an early stage. The DSGE models especially designed for the economic and social features of these economies are hard to find. In this thesis, we develop a new DSGE model with special consideration of the economic and social features of emerging market economies, and account for some of the DSGE problems. The major development and innovation of this thesis is the heterogeneities not only on the supply side but also in terms of households. Additionally, the housing market and real estate assets are explicitly introduced into our model. Thirdly, we introduce a financial sector into our final model. In this sector, financial frictions are included and entrepreneurs are no longer riskless. Financial intermediates take deposits from households and then lend them to entrepreneurs at an interest rate, which is higherthanthedepositrate. Armed with these developments and improvements,the complete model in this thesis is expected to produce better empirical results and thereby more accurate explanation of economic movements in emerging market economies. Based on these models and data samples, we are able to make empirical analysis on the target economies, namely Brazil, China and India. In conclusion, the models developed in this thesis, based essentially on the DSGE type, can be the pioneer dynamic macroeconomic models for emerging market economies such as Brazil, India, and China. Based on these models, we conduct empirical analyses on data from China, Brazil, and India. We use the Bayesian estimation methodology to identify parameters in our model. The empirical results of these newly developed models show a good coherence with our theoretical hypotheses. Additionally, the performance of these models is consistent with the observed samples and the stylized facts in Brazil, China and India in terms of economic features, such as standard deviations of important economic variables including GDP and fixed asset investment. The results are promising, indicating that our DSGE type of model successfully captures the major economic features and dynamics in these countries with improved accuracy and explanatory power.

The three chapters of my dissertation study the effect of access to credit on economic volatility and welfare, and the implications for policy. Chapter 1 presents a unified framework to analyze debt relief and macroprudential policies in a liquidity trap when households have private information. I develop a model with a deleveraging-driven recession and a liquidity trap in which households differ in their impatience, which is unobservable. Ex post debt relief stimulates the economy, but anticipated debt relief encourages overborrowing ex ante, making savers worse off. Macroprudential taxes and debt limits prevent the recession, but can harm impatient households, since the planner cannot directly identify and compensate them. I solve for optimal policy, subject to the incentive constraints imposed by private information. Optimal allocations can be implemented either by providing debt relief to moderate borrowers up to a maximum level, combined with a marginal tax on debt above the cap, or with ex ante macroprudential policy - a targeted loan support program, combined with a tax on excessive borrowing. These policies are ex ante Pareto improving in a liquidity trap; in normal times, however, they are purely redistributive. These results extend to economies with aggregate uncertainty, alternative sources of heterogeneity, and endogenous labor supply. The second chapter of my dissertation presents a theoretical framework to understand sovereign debt crises in a monetary union and the optimal policy response to these crises. The risk of default encourages indebted countries to pay down their short term debt, depressing consumption demand throughout the union. This fall in demand can cause the monetary union to hit the zero lower bound on nominal interest rates, leading to a union-wide recession. I evaluate three policies to prevent such a recession: debt relief, which writes off a portion of short term debt; lending policy, which allows indebted countries to issue new debt at above-market prices; and debt postponement, which converts short into long term debt. I show that if countries can be prevented from retrading in secondary markets after debt restructuring, all three policies are equivalent, and are welfare improving. If retrading is possible, lending policy and debt postponement are superior to debt relief. The final chapter of my dissertation evaluates the impact of increased income uncertainty and financial liberalization in the US on consumption volatility and welfare at the household level. In this joint work with Olga Gorbachev, we estimate Euler equations using consumption data from the Panel Study of Income Dynamics, and measure the volatility of unpredictable changes in consumption as the squared residuals. We directly control for liquidity constraints using data on access to credit from the Survey of Consumer Finances, and document that despite the increase in household debt between 1983 and 2007, there was no decline in the proportion of liquidity constrained households. Consumption volatility increased significantly over this period, especially for liquidity constrained households, indicating substantial welfare losses.

This dissertation contains three essays on Macroeconomics. Detailed micro-level data is used in all three essays. The first chapter studies wealth inequality problems. More specif- ically, it focuses on capital return inequality among university endowments. It combines university-level data on endowment size, capital returns, and portfolio allocations into a unified dataset. Using panel data regression, I show a strong impact of size on investment return. Everything else the same, the biggest endowment has a capital return 8 percent higher than the smallest endowment. However, after adjusting for risk using Sharpe ratios, the strong positive correlation turns negligible or even negative. This result suggests that the higher return of bigger endowments can be attributed to risk compensation rather than to an informational premium. The second and the third chapters employ firm-level data to study macroeconomic pro- ductivity. The second chapter documents the sectoral growth paths of measured total factor productivity (TFP) in southern Europe during the boom that proceeded the great contraction (1996 to 2007). Using both aggregate and firm-level panel data, I show that TFP in sectors that displayed fast expansion, such as construction, dropped significantly, while in non- expanding sectors, such as manufacturing, it stayed stable. I evaluate the relevance of two alternative explanations of this phenomenon: capital misallocation (the increase in capital was directed to less productive firms) and labor quality mismeasurement (lower quality of incoming labor was not fully captured in the TFP calculation). I find that the misalloca- tion channel is almost negligible. Moreover, worker-firm matched data shows that labor quality did deteriorate in the expanding sectors but not in the others, giving credence to the labor-quality mismeasurement hypothesis. A model featuring both the misallocation and the mismeasurement channels and calibrated to match the micro-level productivity distri- bution and labor quality distribution predicts that the drop in true TFP was small if labor quality is measured properly. The third chapter documents the total factor productivity growth path in China from 1998 to 2015 using both the aggregate and the firm-level data. We find that measured TFP growth is positive from 1998 to 2011, before turning flat and even negative. A care- ful comparison between state-owned enterprises (SOEs) and private firms reveals that the slowing down of TFP growth of SOEs is the major contributor to the TFP growth reversal of the whole manufacturing sector. The reversal is not due to changes in the composition of production in different sub-sectors, but mostly due to changes within existing firms.

This Ph.D. dissertation contains three essays on Open Economy Macroeconomics. The first chapter investigates monetary policy problem of emerging economies known as the Tosovsky Dilemma, which says that when an emerging economy experiences a boom associated with capital inflows and exchange rate appreciation, it is not appealing to tighten monetary policy to counteract inflationary pressures as this might further exacerbate inflows and appreciation. In the chapter, I develop an intertemporal general equilibrium framework of the monetary transmission mechanism to investigate how this dilemma shapes optimal monetary policy. In the model, financing is decentralized and collateralized by physical capital, which is nontradable and costly to adjust over time. The Dilemma materializes when there is a positive external shock that increases capital inflows and generates real exchange rate appreciation and inflation in the nontradable sector, all of which are inefficient. Contrary to conventional wisdom, the Ramsey optimal monetary policy calls for lowering the policy rate in such circumstances in order to suppress capital inflows and appreciation, while accepting inflation in the nontradable sector. If the capital flows can be controlled by an additional policy instrument, then optimal policy becomes countercyclical, as in the conventional framework without the Dilemma. The second and third chapters focus on dynamics of labor shares over the business cycles in small open economies. The second chapter uses annual labor shares data of 40 years for 35 small open economy countries and finds three empirical regularities. First, labor shares are not constant, but they are as volatile as output. Second, labor shares in emerging economies are about twice as volatile as labor shares in advanced economies. Third, labor shares in emerging economies are procyclical on average, whereas they are countercyclical in most advanced economies. The empirical findings offer a skeptical view of the conventional beliefs about the unitary elasticity of substitution between capital and labor, and countercyclical labor shares in the short-run. The third chapter paper builds a theoretical model which can comprehensively explain the empirical findings in the second chapter. The model is a dynamic stochastic general equilibrium, small open economy, composed of tradable and nontradable sectors with CES production functions. In the model, there are two margins of labor share fluctuations over the business cycles, which are fluctuations of the capital-labor ratio in each sector and fluctuations in the relative value of sectoral production. The estimated models show a countercyclical labor share and volatility near that of output in Canada, and procyclical and excessively volatile labor share in Mexico, all of which are in line with the data.

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This dissertation contains both theoretical and applied econometric work. The applications are on finance and macroeconomics. Each chapter utilizes time series techniques to analyze dynamic characteristics of data. The first chapter is on composite likelihood (CL) estimation, which has gained a lot of attention in the statistics field but is a relatively new technique to the economics literature. I study its asymptotic properties in a complex dynamic nonlinear model and use it to analyze corporate bond ratings. The second chapter explores the importance of global food price fluctuations. In particular, I measure the effects of global food shocks on domestic macroeconomic variables for a large number of countries. The third chapter proposes a method to interpret latent factors in a data-rich environment. In the application, I find five meaningful factor driving the US economy. Chapter 1, persistent discrete data are modeled by Autoregressive Probit model and estimated by CL estimation. Autocorrelation in the latent variable results in an intractable likelihood function containing high dimensional integrals. CL approach offers a fast and reliable estimation compared to computationally demanding simulation methods. I provide consistency and asymptotic normality results of the CL estimator and use it to study the credit ratings. The ratings are modeled as imperfect measures of the latent and autocorrelated creditworthiness of firms explained by the balance sheet ratios and business cycle variables. The empirical results show evidence for rating assignment according to Through-the-cycle methodology, that is, the ratings do not respond to the short-term fluctuations in the financial situation of the firms. Moreover, I show that the ratings become more volatile over time, in particular after the crisis, as a reaction to the regulations and critics on credit rating agencies. Chapter 2, which is a joint work with Bilge Erten, explores the sources and effects of global shocks that drive global food prices. We examine this question using a sign-restricted SVAR model and rich data on domestic output and its components for 82 countries from 1980 to 2011. After identifying the relevant demand and supply shocks that explain fluctuations in real food prices, we quantify their dynamic effects on net food-importing and food-exporting economies. We find that global food shocks have contractionary effects on the domestic output of net food importers, and they are transmitted through deteriorating trade balances and declining household consumption. We document expansionary and shorter-lived effects for net food exporters. By contrast, positive global demand shocks that also increase real food prices stimulate the domestic output of both groups of countries. Our results indicate that identifying the source of a shock that affects global food prices is crucial to evaluate its domestic effects. The adverse effects of global food shocks on household consumption are larger for net food importers with relatively high shares of food expenditures in household budgets and those with relatively high food trade deficits as a share of total food trade. Finally, we find that global food and energy shocks jointly explain 8 to 14 percent of the variation in domestic output. Chapter 3, which is a joint work with Sinem Hacioglu, exploits a data rich environment to propose a method to interpret factors which are otherwise difficult to assign economic meaning to by utilizing a threshold factor-augmented vector autoregression (FAVAR) model. We observe the frequency of the factor loadings being induced to zero when they fall below the estimated threshold to infer the economic relevance that the factors carry. The results indicate that we can link the factors to particular economic activities, such as real activity, unemployment, without any prior specification on the data set. By exploiting the flexibility of FAVAR models in structural analysis, we examine impulse response functions of the factors and individual variables to a monetary policy shock. The results suggest that the proposed method provides a useful framework for the interpretation of factors and associated shock transmission.