Este estudo têm como objetivo identificar qual o comportamento de empresas brasileiras de capital aberto com relação à aplicação dos recursos financeiros de seus fluxos de caixa (recursos internos) em investimentos e não investimentos (em fins que não se configuram como um projeto real de investimento) no curto e longo prazo e mostrar como essa questão está relacionado com o grau de restrições financeiras enfrentado pelas empresas. Para alcançar esse objetivo foram estimadas quatro regressões pelo método OLS (Ordinary Least Square / Mínimos Quadrados Ordinários), cada uma delas com uma das variáveis resposta correspondentes aos principais usos de caixa, ou seja, retenção de caixa, investimentos, dividendos e redução do financiamento externo e com as variáveis explicativas dadas pelo fluxo de caixa nos períodos t, t-1 e t-2 e algumas variáveis de controle específicas da firma. Os resultados sugerem que empresas brasileiras restritas e irrestritas se comportam de forma diferente ao receberem um choque positivo em seus fluxos de caixa e que o comportamento das mesmas também difere no curto e no longo prazo. Empresas restritas e irrestritas ao receberem um choque positivo em seus fluxos de caixa, retêm caixa no período contemporâneo e alocam tais recursos intertemporalmente. Empresas restritas investem mais no curto prazo enquanto as irrestritas investem mais no longo prazo. No curto prazo, empresas irrestritas distribuem mais dividendos do que empresas irrestritas e no longo prazo, os coeficientes dos fluxos de caixa não são significativos para nenhum dos grupos. No curto prazo empresas irrestritas reduzem o financiamento externo, enquanto empresas restritas levantam mais financiamentos externos e no longo prazo, esse comportamento se inverte. Dessa forma, fica clara a importância de se considerar o longo prazo bem como as restrições financeiras enfrentadas pelas empresas. / This study aim to identify what is the behaviour of Brazilian public companies regarding the use of financial resources of cash flows (internal resources) in investments and not investments (for purposes that are not configured as a real investment project) in the short and long term and show how this is related to the degree of financial constraints faced by firms. To achieve this aim, four regressions were estimated by OLS ( Ordinary Least Square), each with one of the response variables corresponding to the main uses of cash, ie , cash holding, investments, dividends and external finance reduction and the explanatory variables given by the cash flow in periods t , t - 1 and t - 2 and some control variables specific of the firm. The results suggest that restricted and unrestricted Brazilian companies behave differently when they receive a positive shock on cash flows and their behavior also differs in the short and long term. When constrained and unconstrained firms receive a positive impact on cash flows, they retain cash in the contemporary period and intertemporally allocate such resources. Constrained firms invest more in the short term while the unrestricted invest more in the long run. In the short term, unconstrained firms distribute more dividends than unconstrained firms and in the long run, the coefficients of cash flows are not significant for either groups. In the short term unconstrained firms reduce external finance, while constrained firms raise more external finance and in the long term, this behavior is reversed. Thus, it is clear that it matters to consider the long term as well as financial constraints faced by firms.

This thesis employs the Autoregressive Distributed Lag model (ARDL), Toda-Yamamoto causality analysis, and ordinary least square (OLS for robust estimation) techniques to empirically investigate the impact of economic growth and energy consumption on the environment in Nigeria from 1980 to 2020. The results of cointegration demonstrate a long-term link between the model's input variables. The outcome of the first objective of the study shows that trade and economic development in Nigeria worsen the state of the environment. Environmental quality is accelerated by financial development; nevertheless, FDI is proven to be insignificant in predicting environmental quality. The result demonstrates that FDI and energy use both have the potential to significantly speed up the rate of environmental degradation. Nevertheless, trade has a negligible impact on the environment in the country, and financial development slows down environmental deterioration. The study also finds that the combination between energy and economic development improves Nigeria's environmental quality. The outcome of the fourth objective shows that economic expansion and energy consumption have a favorable impact on the environment. Additionally, environmental degradation, energy use, and economic growth are all causally related. Moreover, the outcome of the robust estimation reveals a positive and significant relationship between economic growth and energy consumption in the environment. Therefore, the study suggests economic policies with environmental control measures. This could be through an emphasis on the use of other alternatives of low-emission energy, that will mitigate the level of C02 and enhance energy utilization for a better environment in the nation.

The main purpose of this study is to empirically assess the relationship between inflation and economic growth in Ethiopia using quarterly dataset from 1992Q1 to 2010Q4. In doing so, an interesting policy issue arises. What is the threshold level of inflation for the Ethiopian economy? Based on the Engle-Granger and Johansen co-integration tests it is found out that there is a positive long-run relationship between inflation and economic growth. The error correction models show that in cases of short-run disequilibrium, the inflation model adjusts itself to its long-run path correcting roughly 40% of the imbalance in each quarter. In addition, based on the conditional least square technique, the estimated threshold model suggests 10% as the optimal level of inflation that facilitates growth. An inflation level higher or lower than the threshold level of inflation affects the economic growth negatively and hence fiscal and monetary policy coordination is vital to keep inflation at the threshold. / Economics / M. Com. (Economics)

The main purpose of this study is to empirically assess the relationship between inflation and economic growth in Ethiopia using quarterly dataset from 1992Q1 to 2010Q4. In doing so, an interesting policy issue arises. What is the threshold level of inflation for the Ethiopian economy? Based on the Engle-Granger and Johansen co-integration tests it is found out that there is a positive long-run relationship between inflation and economic growth. The error correction models show that in cases of short-run disequilibrium, the inflation model adjusts itself to its long-run path correcting roughly 40% of the imbalance in each quarter. In addition, based on the conditional least square technique, the estimated threshold model suggests 10% as the optimal level of inflation that facilitates growth. An inflation level higher or lower than the threshold level of inflation affects the economic growth negatively and hence fiscal and monetary policy coordination is vital to keep inflation at the threshold. / Economics / M. Com. (Economics)

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The evolution of third-party logistics has sparked much academic interest over the years, with researchers looking at how the industry has grown and the factors that have contributed to it. The primary goal of this thesis is to examine the factors that have driven third-party logistics companies’ growth in Sweden for eighteen years (2002–2020). Growth in this thesis is considered as the growth of net sales. A total of 924 observations of ninety-five active third-party logistics companies operating in the Swedish market are analyzed in this thesis. An Ordinary Least Squares (OLS) linear regression analysis is carried out to examine the growth factors of third-party logistics companies in Sweden, taking into consideration the direct effect of the variables, number of employees, liabilities, operating profit, Earnings Before Interest and Taxes (EBIT), accessibility, technology, reverse logistics, service offer, customer relationship management (CRM), marketing, and distribution systems on the growth of net sales. Results from the OLS model shows that the size of the company, profitability, and the company's level of competence, has a significant positive influence on third-party logistics (3PL) company's growth.

In this work, we study and extend algorithms for Sparse Regression and Causal Inference problems. Both the problems are fundamental in the area of Data Science. The goal of regression problem is to nd out the \best" relationship between an output variable and input variables, given samples of the input and output values. We consider sparse regression under a high-dimensional linear model with strongly correlated variables, situations which cannot be handled well using many existing model selection algorithms. We study the performance of the popular feature selection algorithms such as LASSO, Elastic Net, BoLasso, Clustered Lasso as well as Projected Gradient Descent algorithms under this setting in terms of their running time, stability and consistency in recovering the true support. We also propose a new feature selection algorithm, BoPGD, which cluster the features rst based on their sample correlation and do subsequent sparse estimation using a bootstrapped variant of the projected gradient descent method with projection on the non-convex L0 ball. We attempt to characterize the efficiency and consistency of our algorithm by performing a host of experiments on both synthetic and real world datasets. Discovering causal relationships, beyond mere correlation, is widely recognized as a fundamental problem. The Causal Inference problems use observations to infer the underlying causal structure of the data generating process. The input to these problems is either a multivariate time series or i.i.d sequences and the output is a Feature Causal Graph where the nodes correspond to the variables and edges capture the direction of causality. For high dimensional datasets, determining the causal relationships becomes a challenging task because of the curse of dimensionality. Graphical modeling of temporal data based on the concept of \Granger Causality" has gained much attention in this context. The blend of Granger methods along with model selection techniques, such as LASSO, enables efficient discovery of a \sparse" sub-set of causal variables in high dimensional settings. However, these temporal causal methods use an input parameter, L, the maximum time lag. This parameter is the maximum gap in time between the occurrence of the output phenomenon and the causal input stimulus. How-ever, in many situations of interest, the maximum time lag is not known, and indeed, finding the range of causal e ects is an important problem. In this work, we propose and evaluate a data-driven and computationally efficient method for Granger causality inference in the Vector Auto Regressive (VAR) model without foreknowledge of the maximum time lag. We present two algorithms Lasso Granger++ and Group Lasso Granger++ which not only constructs the hypothesis feature causal graph, but also simultaneously estimates a value of maxlag (L) for each variable by balancing the trade-o between \goodness of t" and \model complexity".

The study was conducted across the four district municipalities in the Free State province of South Africa. The objective of the study was to determine socio-economic factors that affected livestock numbers among smallholder cattle and sheep farmers in the Free State province of South Africa. The research was qualitative and quantitative in nature. Proportionate random sampling method was used to collect data. The population comprised of smallholder cattle and sheep farmers that kept at least 30 livestock. Data between the 2008 and 2012 farming seasons were collected by administering well-structured questionnaires to 250 smallholder cattle and sheep farmers. Data collected were captured and analysed using the statistical package for social sciences (SPSS version 22 of 2013) to obtain frequency, cross-tabulation, descriptive statistics and ordinary least square (OLS) regression. Descriptive statistics results indicated that lack of camp systems, drought prevalence, increased feed costs, poor veterinary interventions, insufficient breeding stock, high cost of fuel and transportation, lack of equipment, diseases, stock theft and pilfering, and insufficient grazing land were the prevalent factors that affected cattle and sheep farming in the province.The OLS regression results indicated that the variables that significantly affected livestock numbers were district, household size, livestock numbers in 2008, planted pastures, grazing land condition, grazing land acquisition, service, advice / training, veterinary services, purchase of dosing products and sales per year. The results also indicated that the majority (96.8%) of the smallholder cattle and sheep farmers would like to increase their livestock numbers. It was therefore recommended that extension and veterinary services should be strengthened in the study area. In addition, it was recommended that smallholder livestock farmers should be encouraged to plant pastures to reduce pressure on the natural veld and make forage available throughout the year. Lastly, as a recommendation, government should provide subsidies with distribution policies that will ensure that all smallholder livestock farmers can benefit. / Agriculture and  Animal Health / M. Sc. (Agriculture)

The study was conducted across the four district municipalities in the Free State province of South Africa. The objective of the study was to determine socio-economic factors that affected livestock numbers among smallholder cattle and sheep farmers in the Free State province of South Africa. The research was qualitative and quantitative in nature. Proportionate random sampling method was used to collect data. The population comprised of smallholder cattle and sheep farmers that kept at least 30 livestock. Data between the 2008 and 2012 farming seasons were collected by administering well-structured questionnaires to 250 smallholder cattle and sheep farmers. Data collected were captured and analysed using the statistical package for social sciences (SPSS version 22 of 2013) to obtain frequency, cross-tabulation, descriptive statistics and ordinary least square (OLS) regression. Descriptive statistics results indicated that lack of camp systems, drought prevalence, increased feed costs, poor veterinary interventions, insufficient breeding stock, high cost of fuel and transportation, lack of equipment, diseases, stock theft and pilfering, and insufficient grazing land were the prevalent factors that affected cattle and sheep farming in the province.The OLS regression results indicated that the variables that significantly affected livestock numbers were district, household size, livestock numbers in 2008, planted pastures, grazing land condition, grazing land acquisition, service, advice / training, veterinary services, purchase of dosing products and sales per year. The results also indicated that the majority (96.8%) of the smallholder cattle and sheep farmers would like to increase their livestock numbers. It was therefore recommended that extension and veterinary services should be strengthened in the study area. In addition, it was recommended that smallholder livestock farmers should be encouraged to plant pastures to reduce pressure on the natural veld and make forage available throughout the year. Lastly, as a recommendation, government should provide subsidies with distribution policies that will ensure that all smallholder livestock farmers can benefit. / Agriculture and  Animal Health / M. Sc. (Agriculture)