Three Essays On Late-2000S Crises

Three key issues have raised wide attention during the Great Recession. They are jobless recoveries in the U.S., the excessive leverage of global banking sector, and sovereign defaults in Europe. This dissertation studies each of them by chapter, yet they are all related to financing, from firms' financial conditions, to banks' borrowing, to countries' debt. Together, they concern about the relation between macroeconomics and finance from different angles. First, U.S. employment has recovered 3-6 quarters later relative to output recoveries in the post-1990 period, this has not happened before 1990s during the post-war period, which is what many call jobless recoveries. My first study explores how much firms' financial conditions (i.e., borrowing capacity) and firm-paid employee benefits (including health insurance cost) have contributed to the jobless recoveries, using a dynamic stochastic general equilibrium (DSGE) model. The paper makes four main contributions: 1) I document tighter financial conditions during recent three recoveries comparing to the ones before and show its impact on jobless recoveries and employment volatilities. 2) I document the underexplored cyclicality of per worker benefit costs and show that the costs decline during recessions and increase during recoveries. Moreover, the increases of per worker benefit costs during recent recoveries have become larger. 3) Using the financial conditions, the pro-cyclicality of benefit costs, and the costs' rising trend, this model produces 3-to-7-quarter delays in employment recoveries relative to business cycle troughs for the 1990, 2001, and 2007 recessions and no delay for the pre-1990 period. This is consistent with the data that has scarcely been matched in previous literature. 4) The calibrated model generates more than 76 percent of employment volatility, as well as most of the volatility in per worker hours and in output. The second study, coauthored with Ruud de Mooij and Tigran Poghosyan, explores how corporate taxes affect the capital structure of multinational banks. Guided by a theory of optimal capital structure, it tests (i) whether local taxes induce subsidiary banks to raise leverage in light of traditional debt bias; and (ii) whether cross-country tax differences affect intra-bank capital structure through international debt shifting. Using a novel data set for 558 commercial bank subsidiaries of the 86 largest multinational banks in the world, we find that taxes matter significantly, through both the debt bias channel and the international debt shifting. Our results imply that taxation causes international debt spillovers through multinational banks. Last, there has been a long established relationship between default and international trade in the empirical literature, however, its theoretical counterpart is scarce. My third study models the trade impact of endogenous default in a stochastic dynamic framework of two open economies that features incomplete financial markets and currency crisis (exchange rate depreciation). In the model, the exchange rate collapses due to default, therefore affecting GDP and goods trade. It predicts post-default deteriorating imports and rising exports, which is consistent with the data. This paper can be used to study both defaulter's and creditor's welfare

Real Convergence in the European Union

Over the next couple of years, the European Union will face a difficult stage, being confronted with the eventual transition to a monetary union. In the beginning of 1997, it is less clear than ever, if and when the European Monetary Union will eventually be realized, which countries will join in this process, and which countries will benefit from monetary union or are likely to loose out. Using econometric methods, the work attempts to assess the real economic effects of the European Monetary Union. In a first step, differences in labor and goods market adjustment processes between the fifteen member states of the European Union, the United States and Canada are studied in order to evaluate the short-term prospects of monetary union. Turning to the long-run effects, within a second step, convergence of living standards is assessed

The dynamics of market structure and R&D competition

This thesis investigates the articulation of ~he incentives to perform Research and Development of profit seeking firms. Throughout the thesis, the dynamic evolution of the distribution of these incentives across firms is the engine of industry transformation and growth. Thus, in order to a.'5SesS the impact of different industry characteristics on the market structure, we need a faithful picture of the context where firms make their R&D choices. Chapter.one exposes more in detail the motivation to pursue the analysis developed in each chapter independently, and how they combine to build up' the search for the understanding of the interactions between R&D, appropriability and market structure. Chapter two presents a dynamic model of the firm size distribution. Empirical studies of the firm size distribution often compare its moments to those of a log-normal distribution, as implied by Gibrat's Law, and note important deviations. Thus, the first and basic questions addressed in the first chapter are how well does the dynamic industry model reproduce Gibrat's Law and how well does it match the deviations uncovered in the literature. We show that the model reproduces these results when testing the simulated output USing the techniques, of the empirical literature. We then use the model to study how structural parameters affect the firm size distribution. We find that, among other things, fixed and sunk costs increase both the mean and variance of the firm size distribution while generally decreasing the skewness and kurtosis. The rate of growth in an industry also raises the mean and variance, but has non-monotonic effects on the higher moments. In the third chapter we explore the implications of different degrees of R&D appropriability on market structure and welfare. We propose a framework to -- pursue this analysis by extending the ¥arkov-Perfect dynamic industry model proposed by Ericson and Pakes (E-P, henceforth) (1995) through the introdu~tion of a non-proprietary productivity component to R&D as part of a dynamic, stochastic process. We first assume that spillovers are costlessly absorbed and exploited by firms in the industry, and find that, in this case, a free rider problem arises, thereby decreasing the incentives for investment. This leads to a lower amount of innovations being developed in the industry, which in turn, implies lower consuriier welfare while leaving the degree of concentration in the industry fairly unchanged. We then model a settmg where it is assumed that in order to build its absorptive capacity the firm has to engage in some R&D of its own. In this case, we find that an increase in spillovers will enhance both consumer and producer welfare substantially, and increases the likelihood of neck-and-neck competition, therefore reduciJ;lg the level of concentration in the industry. These results arise from the fact that having absorptive capacity being.built as a byproduct ofR&D enhances the productivity ofR&D investment, compensating for the free rider effect associated with the lack of appropriability. The frameworks used in the two first chapters suffer from the 'curse of dimensionality', such that the industries under analysis are limited in terms of the number of agents simultaneously active. In order to overcome this problem, in chapter four we move away from oligopolistic market structures and propose a model of monopolistic competition, where firms are sufficiently large to generate a firm size distribution with a certain degree of asYIDlIletry, although each firm is too small to affect the industry's outcome. Furthermore, we account for industry growth by having the industry's output increasing over time as a result ofknowledge externalities. The rich micro set-up of this model is analogous to .that of E-P (1995), as it is composed by heterogeneous :firms making their in- .~ vestment decisions in a world of uncertainty, but we abstract from entry and exit and instead of an oligopolistic market structure we model a monopolistic competition environment with many, heterogeneous firms. In thiS setting, :firms are asYmmetric in terms of the technology they use to produce a given commodity, and they are able to increase the likelihood of decreasing their marginal costs of production by investing in Cost Reducing R&D. In order to evaluate their future stream of profits and make their investment decisions :firms only care about the eV9lution of their efficiency and the long-run efficiency index in the industry. Cutting down the oligopolistic interactions present in the E-P framework, and having firms looking at the long-run average industry state, allows us to overcome the curse of dimensionality usually associated with dynamic models with agent heterogeneity. Therefore, we are able to simulate the model with a large number of firms competing in the industry and we show that, contrary to most existing endogenouS growth models, this model is able to deliver a firm size distribution with a substantial degree of heterogeneity. Chapter 6 presents the final remarks to the investigation carried out in this thesis

Essays in econometrics

This dissertation contributes to the theoretical understanding and practical application of non- and semi-parametric methods in econometrics. It consists of three chapters. The first chapter advocates the use of unsupervised statistical learning (clustering) techniques to group observations from a series of repeated cross-sections to create a pseudo-panel of group averages. This clustering method is based on features of the data space and does not require external grouping variables unlike many other methods. Using a model of enterprise training as an example, fixed eff ects panel data model is estimated using a pseudo-panel of cluster centers. Chapters 2 and 3 extend univariate kernel methods to the estimation of time-varying distributions and densities subject to moment constraints. Chapter 2 proposes a weighted kernel density estimator for a time-varying probability density function and the corresponding cumulative distribution function. Time-varying quantiles are estimated by inverting an estimate of the cumulative distribution function. Weighting schemes are derived from those used in time series modelling. Parameters, including the bandwidth, may be estimated by maximum likelihood or cross-validation. Diagnostic checks are constructed based on residuals given by the predictive cumulative distribution function. Chapter 3 considers a set-up where additional information concerning the distribution of random variables is available in the form of moment conditions. A weighted kernel density estimate reflecting the extra information is constructed by replacing the uniform weights associated with standard kernel density estimator by generalised empirical likelihood implied probabilities. This chapter shows that the resulting density estimator provides an improved approximation to the moment conditions. Moreover, a reduction in variance is achieved due to the systematic use of the extra moment information

Continuous record Laplace-based inference about the break date in structural change models

Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2018a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. It is defined by an integration rather than an optimization-based method.A transformation of the least-squares criterion function is evaluated in order to derive a proper distribution, referred to as the Quasi-posterior. For a given choice of a loss function, the Laplace-type estimator is the minimizer of the expected risk with the expectation taken under the Quasi-posterior. Besides providing an alternative estimate that is more precise—lower mean absolute error (MAE) and lower root-mean squared error (RMSE)—than the usual least-squares one, the Quasi-posterior distribution can be used to construct asymptotically valid inference using the concept of Highest Density Region. The resulting Laplace-based inferential procedure is shown to have lower MAE and RMSE, and the confidence sets strike the best balance between empirical coverage rates and average lengths of the confidence sets relative to traditional long-span methods, whether the break size is small or large.First author draf

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

On the covariance structure and mobility of Italian wages

This Thesis uses Italian panel micro-data to investigate the intertemporal wage covariance structure and the extent of transition probabilities at the bottom of the wage distribution, producing new and original evidence on the degree of persistence of cross-sectional wage differentials over individual life-cycles and on the features of the process governing wage mobility across low-pay thresholds. Chapter 1 presents a survey of the debate generated by the rise of wage inequality observed in many industrialised economies and stresses how longitudinal analyses of wage persistence and mobility shed light on the long term impact of rising cross-sectional dispersion; a survey of the two research areas to which this Thesis contributes, i.e. variance components models of the wage covariance structure and econometric modelling of transition probabilities, is also presented. Variance components models of the wage covariance structure are estimated in Chapters 2 and 3, where two unbalanced panels drawn from the Social Security archive on the 1974-88 and 1979-95 intervals (respectively) are analysed by applying the minimum distance technique. Chapter 2 shows that while permanent wage profiles converged within the overall wage distribution, divergence can be detected for white collar workers, suggesting that the former could have been imparted by the egalitarian wage policies of the late 1970s. Results from Chapter 3 indicate that the rising wage inequality observed in Italy over the 1980s and the early 1990s permanently affected the evolution of wage profiles especially during the second half of the 1980s; on the other hand, increases in the relative importance of wage volatility are shown to characterise the first half of the 1990s, thus mirroring the higher labour market “flexibility" of recent years. The Chapter also takes into account the relationship between covariance structure components and observable workers characteristics; in particular, a model which shifts the parameters of interest with respect to workers’ occupations is developed, finding that permanent differentials arise from the wage distribution of white collar workers. A bivariate probit model with endogenous switching is developed in Chapter 4 to analyse low-wage mobility taking the endogeneity of starting wage states into account, using survey data from the Bank of Italy. Results indicate the appropriateness of such a framework, the correlation between state and transition probabilities being statistically significant. While workers’ attributes are found to have a limited impact on the probability of leaving low-pay, a considerable share of aggregate low-pay persistence appears to be the consequence of true state dependence, i.e. the experience of low-pay raises, per se, the likelihood that the phenomenon occur in the future. Chapter 5 checks the robustness of these conclusions to the presence of endogenous attrition from the wage distribution over time by augmenting the model with a third equation for the probability of belonging to the balanced sample. The computational difficulties posed by the required evaluation of trivariate normal integrals are overcome by implementing simulation estimation techniques. Results indicate that exits from the wage distribution over time are an ignorable source of sample selection for the estimation of low-pay transition probabilities on these data, thus pointing towards the robustness of the findings of Chapter 4 to this generalization of the model