871 resultados para Panel data probit model


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Méthodologie: Modèle de régression quantile de variable instrumentale pour données de Panel utilisant la fonction de production partielle

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This thesis examines firms' real decisions using a large panel of unquoted euro area firms over the period 2003-2011. To this end, this thesis is composed of five chapters in which three are the main empirical chapters. They assess the dimensions of firm behaviour across different specifications. Each of these chapters provide a detailed discussion on the contribution, theoretical and empirical background as well as the panel data techniques which are implemented. Chapter 1 describes the introduction and outline of the thesis. Chapter 2 presents an empirical analysis on the link between financial pressure and firms' employment level. In this set-up, it is explored the strength of financial pressure during the financial crisis. It is also tested whether this effect has a different impact for financially constrained and unconstrained firms in the periphery and non-periphery regions. The results of this chapter denote that financial pressure exerts a negative impact on firms' employment decisions and that this effect is stronger during the crisis for financially constrained firms in the periphery. Chapter 3 analyses the cash policies of private and public firms. Controlling for firm size and other standard variables in the literature of cash holdings, empirical findings suggest that private firms hold higher cash reserves than their public counterparts indicating a greater precautionary demand for cash by the former. The relative difference between these two type of firms decreases (increases) the higher (lower) is the the level of financial pressure. The findings are robust to various model specifications and over different sub-samples. Overall, this chapter shows the relevance of firms' size. Taken together, the findings of Chapter 3 are in line with the early literature on cash holdings and contradict the recent studies, which find that the precautionary motive to hold cash is less pronounced for private firms than for public ones. Chapter 4 undertakes an investigation on the relation between firms' stocks of inventories and trade credit (i.e. extended and taken) whilst controlling for the firms' size, the characteristics of the goods transacted, the recent financial crisis and the development of the banking system. The main findings provide evidence of a trade-off between trade credit extended and firms' stock of inventories. In other words, firms' prefer to extend credit in the form of stocks to their financially constrained customers to avoid holdings costly inventories and to increase their sales levels. The provision of trade credit by the firms also depends on the characteristics of the goods transacted. This impact is stronger during the crisis. Larger and liquid banking systems reduce the trade-off between the volume of stocks of inventories and the amount sold on credit. Trade credit taken is not affected by firms' stock of inventories. Chapter 5 presents the conclusions of the thesis. It provides the main contributions, implications and future research of each empirical chapter.

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El presente documento analiza los determinantes del margen de intermediación para el sistema financiero colombiano entre 1989 y 2003. Bajo una estimación dinámica de los efectos generados por variables específicas de actividad, impuestos y estructura de mercado, se presenta un seguimiento del margen de intermediación financiero, para un período que presenta elementos de liberalización y crisis.

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In the last two decades, authors have begun to expand classical stochastic frontier (SF) models in order to include also some spatial components. Indeed, firms tend to concentrate in clusters, taking advantage of positive agglomeration externalities due to cooperation, shared ideas and emulation, resulting in increased productivity levels. Until now scholars have introduced spatial dependence into SF models following two different paths: evaluating global and local spatial spillover effects related to the frontier or considering spatial cross-sectional correlation in the inefficiency and/or in the error term. In this thesis, we extend the current literature on spatial SF models introducing two novel specifications for panel data. First, besides considering productivity and input spillovers, we introduce the possibility to evaluate the specific spatial effects arising from each inefficiency determinant through their spatial lags aiming to capture also knowledge spillovers. Second, we develop a very comprehensive spatial SF model that includes both frontier and error-based spillovers in order to consider four different sources of spatial dependence (i.e. productivity and input spillovers related to the frontier function and behavioural and environmental correlation associated with the two error terms). Finally, we test the finite sample properties of the two proposed spatial SF models through simulations, and we provide two empirical applications to the Italian accommodation and agricultural sectors. From a practical perspective, policymakers, based on results from these models, can rely on precise, detailed and distinct insights on the spillover effects affecting the productive performance of neighbouring spatial units obtaining interesting and relevant suggestions for policy decisions.

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The research approaches recycling of urban waste compost (UWC) as an alternative fertilizer for sugarcane crop and as a social and environmental solution to the solids residuals growth in urban centers. A mathematical model was used in order to know the metal dynamics as decision support tool, aiming to establish of criteria and procedures for UWC's safe use, limited by the amount of heavy metal. A compartmental model was developed from experimental data in controlled conditions and partially checked with field data. This model described the heavy metal transference in the system soil-root-aerial portion of sugarcane plants and concluded that nickel was metal to be concern, since it takes approximately three years to be attenuated in the soil, reaching the aerial portions of the plant at high concentrations. Regarding factors such as clay content, oxide level and soil pH, it was observed that for soil with higher buffering capacity, the transfer of the majority of the metals was slower. This model may become an important tool for the attainment of laws regarding the UWC use, aiming to reduce environment contamination the waste accumulation and production costs.

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The photo-Fenton process (Fe(2+)/Fe(3+), H(2)O(2), UV light) is one of the most efficient and advanced oxidation processes for the mineralization of the organic pollutants of industrial effluents and wastewater. The overall rate of the photo-Fenton process is controlled by the rate of the photolytic step that converts Fe(3+) back to Fe(2+). In this paper, the effect of sulfate or chloride ions on the net yield of Fe(2+) during the photolysis of Fe(3+) has been investigated in aqueous solution at pH 3.0 and 1.0 in the absence of hydrogen peroxide. A kinetic model based on the principal reactions that occur in the system fits the data for formation of Fe(2+) satisfactorily. Both experimental data and model prediction show that the availability of Fe(2+) produced by photolysis of Fe(3+) is inhibited much more in the presence of sulfate ion than in the presence of chloride ion as a function of the irradiation time at pH 3.0.

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Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.

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The zero-inflated negative binomial model is used to account for overdispersion detected in data that are initially analyzed under the zero-Inflated Poisson model A frequentist analysis a jackknife estimator and a non-parametric bootstrap for parameter estimation of zero-inflated negative binomial regression models are considered In addition an EM-type algorithm is developed for performing maximum likelihood estimation Then the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and some ways to perform global influence analysis are derived In order to study departures from the error assumption as well as the presence of outliers residual analysis based on the standardized Pearson residuals is discussed The relevance of the approach is illustrated with a real data set where It is shown that zero-inflated negative binomial regression models seems to fit the data better than the Poisson counterpart (C) 2010 Elsevier B V All rights reserved

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Most regional programs focus on the supply side of regions, emphasizing the attraction conditions offered, such as infrastructure, labor skills, tax incentives, etc. This study analyzes one aspect of the demand side, that is, how investment decisions of private firms are made by asking the question: ""Do corporations decide the same way on investments in different parts of the territory?"" The paper analyzes the investments of 373 large Brazilian firms during 1996-2004. Based on the investment decisions of these firms, the role of sales, cash-flow, external financing, and working capital is investigated through regression analysis. The regional influence is captured by explanatory variables representing regional and firm characteristics, and by interaction dummies between the region and the main investment determinants. The results indicate significant differences across regions in the importance of investment determinants. This information is important for regional development policy, because different mechanisms should be used in different regions to foster private investments.

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The purpose of this article is to examine the causality between government size and corruption, and to verify if there is a different pattern of causality between developed Organization for Economic Co-operation and Development (OECD) countries (excluding Mexico) and developing countries (Latin American countries) during the period 1996 to 2003. Applying Granger and Huang`s (1997) methodology we find evidence that size of government Granger causes corruption in both samples. Since a larger government involvement in private markets today will be followed in future by a higher level of corruption a policy advice would be to enhance governance. The promotion of good governance helps to combat corruption given that it complements efforts to reduce corruption more directly, and it is strongly recommended by the International Monetary Fund, other multilateral institutions, and all worried with the negative impacts of corruption on economic activity.

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This article tests the hypothesis of opportunistic and partisan cycle models using a new large data set of Brazilian municipalities over the 1989-2005 period. The results show an increase in total and current expenditures and a decrease in municipal investments, local tax revenues, and budget surplus in election years. They also show that partisan ideology exerts a relative influence on the performance of the local public accounts. These results confirm that both opportunistic and partisan cycles have occurred in the management of the budgets of Brazilian municipalities after the end of the military government.

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Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

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beta-Carboline imine reductions mediated by Saccharomyces bayanus have been described achieving moderate to good enantiomeric excesses of the amine products. The enantiomeric excesses of the bioreduction showed a dependence on the imine substituents. Compounds presenting C(1)-C(11) aliphatic substituent groups afforded amines with an (S)-configuration, whereas C(15) and higher aliphatic, and aromatic substituted B-carboline imines achieved inversion of the configuration in the final (R)-2 amine products. Based on this data, a model for the Saccharomyces reduction is proposed. (C) 2010 Elsevier Ltd. All rights reserved.

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This article examines the efficiency of the National Football League (NFL) betting market. The standard ordinary least squares (OLS) regression methodology is replaced by a probit model. This circumvents potential econometric problems, and allows us to implement more sophisticated betting strategies where bets are placed only when there is a relatively high probability of success. In-sample tests indicate that probit-based betting strategies generate statistically significant profits. Whereas the profitability of a number of these betting strategies is confirmed by out-of-sample testing, there is some inconsistency among the remaining out-of-sample predictions. Our results also suggest that widely documented inefficiencies in this market tend to dissipate over time.