Whether we would like to model imports and exports, or forecast inflation, structural variation in an economy frequently causes problems. This thesis examines such variation in two dimensions: first, in a cross-section of individuals, and secondly, over time. A panel of manufacturing industries in several developed countries reveals that there is substantial variation across sectors, in the response of trade to changes in prices and incomes. Ignoring this heterogeneity can render conventional results biased and inconsistent, so a number of robust methods are used to obtain reliable estimates of long-run and short-run trade relationships. The findings point to common behaviour across sectors, which could be due to similarities in technology. The impact of structural breaks over time is examined in the second part of the thesis. Unpredictable shifts in deterministic terms such as the mean of a process are shown to generate significant forecast failure, and even the methods used to evaluated forecast accuracy are affected. Using the Kullback-Leibler discrepancy to measure the size of forecast errors, various robust mechanisms are discussed, that do not fail systematically after a break. Although they can provide a degree of insurance if a shift does occur, this comes at a cost if there is no change, and in the presence of measurement error they can exacerbate the uncertainty surrounding a forecast. An empirical illustration with a model of UK money demand provides some support for the automatic correction mechanisms, although there does seem to be a role for direct modeling of a break process.

This thesis deals with the detection of change in the intercept in panel data re- gression model. We are interested in testing a null hypothesis that there was no change in the intercept during the observation period in case with no depen- dency between panels and with the number of panels and observations in each panel going to infinity. Based on the results for simplified case with no additional regressors we propose a statistical test and show its properties. We also derive a consistent estimate of the parameter of change based on the least squares me- thod. The main contribution of the thesis is the derivation of theoretical results of the proposed test while variances of errors are known and its modification for unknown variance parameters. A large simulation study is conducted to examine the results. Then we present an application to real data, particularly we use four factor CAPM model to detect change in monthly returns of US mutual funds during an observation period 2004-2011 and show a significant change during the sub-prime crisis in 2007-2008. This work expands existing results for de- tecting changes in the mean in panel data and offers many directions for further beneficial research. 1

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-14T13:52:48Z No. of bitstreams: 1 raphaeldefreitassaldanha.pdf: 13350574 bytes, checksum: c1efacf54b8e45ed46b5303adf0b2e11 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-16T13:05:45Z (GMT) No. of bitstreams: 1 raphaeldefreitassaldanha.pdf: 13350574 bytes, checksum: c1efacf54b8e45ed46b5303adf0b2e11 (MD5) / Made available in DSpace on 2017-03-16T13:05:45Z (GMT). No. of bitstreams: 1 raphaeldefreitassaldanha.pdf: 13350574 bytes, checksum: c1efacf54b8e45ed46b5303adf0b2e11 (MD5) Previous issue date: 2016-11-01 / A violência no trânsito tem apresentado números preocupantes e crescentes no contexto internacional e brasileiro, sublinhando a importância deste tema para o campo da Saúde Coletiva através do conceito de saúde ampliada. A violência no trânsito encontra raízes na formação histórica da sociedade, onde a introdução do automóvel reflete a estrutura de classes de sua época, cujos reflexos ainda são encontrados na sociedade. O acesso à automóveis e outros meios de transporte através do incremento da renda torna este fator um importante determinante social da saúde para o estudo da violência no trânsito. Este trabalho visa compreender a relação entre o crescimento econômico e a violência no trânsito nos municípios brasileiros, utilizando um painel de dados espaciais de 12 anos. Os resultadosdoModelodeErroEspacialdeDurbinapontamparaaexistênciadeumaCurva de Kuznets nesta relação, onde covariáveis relativas a frota de veículos, investimentos em transportes, urbanização e o IDH são significativas. Estes resultados indicam que a violência no trânsito pode ser influenciada através de políticas públicas que objetivem resultados nestas covariáveis, não requerendo que os municípios atinjam patamares de renda mais elevados para ocasionar a redução da mortalidade no trânsito. / There is a concerning and increasing quantity of traffic violence internationally and in Brazil, underlining the importance of this theme to Public Health through an expanded health concept. Traffic violence finds roots in the historical formation of society, where the introduction of automobiles reflects the arrangement of classes of that time, and these reflections still can be seen in today’s society. Access to automobiles and other means of transport through the increase of wealth makes income an important social determinant of health in the study of traffic violence. This study aims to understand the relationship between the increase of income and traffic violence using a spatial data panel of 12 years. The results of the Spatial Durbin Error Model point to the occurrence of a Kuznets Curve for this relation, where the factors of the vehicle fleet, investments on traffic infrastructure and urbanization and the HDI are significant. As a result, traffic violence can be affected by public policies that target these factors, instead of requiring that the municipalities reaches more elevated levels of income to observe a reduction in traffic violence.

This thesis analyzes determinants of housing construction in regions in the Czech Republic. The main research question is the impact of standard of living on housing construction. The living standard is expressed in terms of net disposable income per capita and housing construction represents the number of housing starts. Other determinants included to the model estimation are rate of unemployment, housing price and number of mortgage. Analysis works with the panel data from period 2005- 2015 and all variables are used in the logarithmic form with one year lag. The model is estimated by random effects model. The assumption about positive impact of living standard on housing construction is not confirmed, because of the statistical insignificance of variable net disposable income. In case of other variables expected effects are confirm. The increases in rate of unemployment and housing prices have the negative impact on housing construction. And opposite the number of mortgage has positive impact on housing construction.

In this paper is tested validity of the neoclassical migration model. For this purpose, were used Fixed effects model and VAR model. Data contain period of years 2001 to 2010 from 14 regions of the Czech republic and dataset contains 140 observations. Empirical results of Fixed effects model show that socioeconomic determinants had signifficant influence on regional rate of migration in the Czech republic. The direction and strength of influence of the most explanatory variables corresponded to the neoclassical theory. Estimations of VAR model indicate that regional migration did not decrease disparities within regions. These results questioned validity of neoclassical migration model.

[pt] A governança corporativa é um conjunto de práticas adotadas pelas empresas visando minimizar os conflitos de agência e riscos relacionados, para à maximização dos rendimentos e por consequência, trazer um maior retorno esperado para os investidores. Em virtude da importância da governança no Brasil e no mundo, buscou-se nessa pesquisa analisar as contribuições dos mais altos níveis de governança na rentabilidade das empresas listadas na B3 no período da pandemia do Covid-19 no ano de 2020. Para atingir tal objetivo, foram coletados os índices de rentabilidade ROA e ROE de cada empresa da amostra do período de 2017 a 2020 para validar se o nível de governança das empresas impactou no desempenho delas e no período analisado de crise econômico-financeira. Adicionalmente, utilizaram-se as variáveis de alavancagem, liquidez e tamanho como variáveis de controle para as empresas das analisadas. Visando verificar a existência de diferenças entres as médias dos índices de rentabilidade em virtude do nível de governança foi utilizado a metodologia de Dados em Painel com efeitos fixos. O resultado da pesquisa sugere que os diferentes níveis de governança não possuem relação significante direta com desempenho econômico-financeiro da empresa. A obtenção desse resultado possivelmente demostra que o motivo relacionado a opção das empresas de obter níveis mais elevados de governança está relacionado à sua capacidade de trazer equidade e transparência na prestação de contas, almejando assim promover maior confiabilidade/credibilidade por parte dos investidores. / [en] Corporate governance is a set of practices used to minimize agency conflicts and risks related to companies seeking to increase income and, consequently, a increase the expected return for investors. Due to the importance of governance in Brazil and in the world, this research wanted to test the contributions of the highest levels of governance to the profitability of companies listed on B3 in the period of impact with the Covid-19 pandemic in the year of 2020. To achieve this goal, the ROA and ROE profitability indexes of each company in the sample were collected to validate whether the level of governance of the companies impacted their performance for the period 2017 to 2020. Additionally, leverage, liquidity and size variables were used as control variables for the companies analysed. Aiming to verify the existence of differences between the averages of the profitability indexes due to the level of corporate governance, the Panel Data methodology with fixed effects was used, as it is understood that due to the choice of data, it would present the best results for the research. The research result suggests that the different levels of governance do not have a direct significant relationship with the company s economic-financial performance. Achieving this result possibly demonstrates that the reason related to the option of companies to obtain higher levels of governance is related to their ability to bring equity and transparency in accountability, thus aiming to promote greater reliability/credibility on the part of the investors.

This work investigates mainly panel data models in which cross-sections can be considered independent. In the first part, we summarize results in the field of pool models and one-way error component models with fixed and random effects. We focus especially on the ways of estimating unknown parameters and on effects significance tests. We also briefly describe two-way error component model issues. In the second part, estimators of first order autoregressive panel data model parameters are derived, for both fixed and random parameters case. The work proves unbiasedness, consistency and asymptotic normality of selected estimators. Using these features, hypothesis tests about corresponding parameters are derived. Application of models is illustrated using real data and simulated data examples. Powered by TCPDF (www.tcpdf.org)

This thesis studies the use of nonparametric econometric methods to reconcile the empirical behaviour of financial asset prices with theoretical valuation models. The confrontation of economic theory with asset price data requires various functional form assumptions about the preferences and beliefs of investors. Nonparametric methods provide a flexible class of models that can prevent misspecification of agents’ utility functions or the distribution of asset returns. Evidence for potential nonlinearity is seen in the presence of non-Gaussian distributions and excessive volatility of stock returns, or non-monotonic stochastic discount factors in option prices. More robust model specifications are therefore likely to contribute to risk management and return predictability, and lend credibility to economists’ assertions. Each of the chapters in this thesis relaxes certain functional form assumptions that seem most important for understanding certain asset price data. Chapter 1 focuses on the state-price density in option prices, which confounds the nonlinearity in both the preferences and the beliefs of investors. To understand both sources of nonlinearity in equity prices, Chapter 2 introduces a semiparametric generalization of the standard representative agent consumption-based asset pricing model. Chapter 3 returns to option prices to understand the relative importance of changes in the distribution of returns and in the shape of the pricing kernel. More specifically, Chapter 1 studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A dynamic kernel estimator of the conditional pricing function and its derivatives is proposed that can be used for model-free risk measurement. Infill asymptotic theory is derived that applies when the pricing function is either smoothly varying or driven by diffusive state variables. Trading times and moneyness levels are modelled by marked point processes to capture intraday trading patterns. A simulation study investigates the performance of the estimator using an iterated plug-in bandwidth in various scenarios. Empirical results using S&P 500 E-mini European option quotes finds significant time-variation at intraday frequencies. An application towards delta- and minimum variance-hedging further illustrates the use of the estimator. Chapter 2 proposes a semiparametric asset pricing model to measure how consumption and dividend policies depend on unobserved state variables, such as economic uncertainty and risk aversion. Under a flexible specification of the stochastic discount factor, the state variables are recovered from cross-sections of asset prices and volatility proxies, and the shape of the policy functions is identified from the pricing functions. The model leads to closed-form price-dividend ratios under polynomial approximations of the unknown functions and affine state variable dynamics. In the empirical application uncertainty and risk aversion are separately identified from size-sorted stock portfolios exploiting the heterogeneous impact of uncertainty on dividend policy across small and large firms. I find an asymmetric and convex response in consumption (-) and dividend growth (+) towards uncertainty shocks, which together with moderate uncertainty aversion, can generate large leverage effects and divergence between macroeconomic and stock market volatility. Chapter 3 studies the nonparametric identification and estimation of projected pricing kernels implicit in the pricing of options, the underlying asset, and a riskfree bond. The sieve minimum-distance estimator based on conditional moment restrictions avoids the need to compute ratios of estimated risk-neutral and physical densities, and leads to stable estimates even in regions with low probability mass. The conditional empirical likelihood (CEL) variant of the estimator is used to extract implied densities that satisfy the pricing restrictions while incorporating the forwardlooking information from option prices. Moreover, I introduce density combinations in the CEL framework to measure the relative importance of changes in the physical return distribution and in the pricing kernel. The nonlinear dynamic pricing kernels can be used to understand return predictability, and provide model-free quantities that can be compared against those implied by structural asset pricing models.

Development of information and communication technologies (ICT) has recently increasing importance. ICT has therefore become one of the effective tools in process of optimization and increase of competitiveness on the market. As a result of this trend, there are already some studies, having ambitions to explicitly formulate their importance. The application of ICT in the Czech Republic is similar, but there are not many studies with such ambitions. This thesis analyzes (i) the relations between, the use of ICT, total factor productivity and growth of gross added value in the Czech Republic in 1997-2007 based on growth accounting and (ii) based on econometric analysis of panel data for the Czech Republic, also estimates the importance of selected determinants influencing adoption of ICT. The output of the thesis consists of (i) determining the contribution of each input and discussion of possible synergies of ICT capital deepening and total factor productivity and (ii) specification of variables that are, based on the formulated model, shown to be statistically significant and they may affect the adoption of ICT.

This master's thesis investigates the impact of exchange rate volatility on trade balance of the Czech Republic during 2005-2016. The analysis is performed on the constructed panel dataset for 53 trading partners of the Czech Republic by estimating the trade gravity models. The realized volatility values are obtained for 43 Czech koruna pairs against the local currencies. The variables included into the empirical analysis are the GDP and population of the Czech Republic and its trading partners, realized volatility, weighted distance, contiguity, direct access to the sea and information on EU and OECD membership. The methodological approaches in the analysis are calculations of realized exchange rate volatility and for gravity models panel data estimation techniques: pooled OLS, fixed effects and random effects. The gravity models are compared by the formal tests, and the most efficient among them is the fixed effects. The results of the estimated augmented model reveal significant positive impact of exchange rate volatility on trade balance of the Czech Republic. The key variables that have the expected significant positive impact on trade balance are GDP of the Czech Republic and its trading partners in the basic model, population of the Czech Republic and EU membership in the augmented model....