Using Nebraska's Beginning Farmer Tax Credit Program

Many farm or ranch families that are attempting to bring a son or daughter back into their business experience a strain on the cash flow. Recent changes to Nebraska's Beginning Farmer Tax Credit Program provide an attractive incentive that can be very beneficial to those families. Regulation changes made in 2008 now allow parents to rent agricultural assets to their own children.

Using Nebraska’s Beginning Farmer Tax Credit Program

Many farm or ranch families that are attempting to bring a son or daughter back into their business experience a strain on the cash flow. After all, a business that has been providing enough income for one family to live on, must now not only generate adequate income for the parents living expenses, but also attempt to provide enough income for a second family, the successor. Recent changes to Nebraska’s Beginning Farmer Tax Credit Program provide an attractive incentive that can be very beneficial for family farming/ranching operations that are trying to bring a family member back into their business. Regulation changes made in 2008 now allow parents to rent agricultural assets to their own children.

Evaluating the legacy of landscape history: extinction debt and species credit in bird and small mammal assemblages in the Brazilian Atlantic Forest

There is now an extensive literature on extinction debt following deforestation. However, the potential for species credit in landscapes that have experienced a change from decreasing to expanding forest cover has received little attention. Both delayed responses should depend on current landscape forest cover and on species life-history traits, such as longevity, as short-lived species are likely to respond faster than long-lived species. We evaluated the effects of historical and present-day local forest cover on two vertebrate groups with different longevities understorey birds and non-flying small mammals - in forest patches at three Atlantic Forest landscapes. Our work investigated how the probability of extinction debt and species credit varies (i) amongst landscapes with different proportions of forest cover and distinct trajectories of forest cover change, and (ii) between taxa with different life spans. Our results suggest that the existence of extinction debt and species credit, as well as the potential for their future payment and/or receipt, is not only related to forest cover trajectory but also to the amount of remaining forest cover at the landscape scale. Moreover, differences in bird and small mammal life spans seem to be insufficient to affect differently their probability of showing time-delayed responses to landscape change. Synthesis and applications. Our work highlights the need for considering not only the trajectory of deforestation/regeneration but also the amount of forest cover at landscape scale when investigating time-delayed responses to landscape change. As many landscapes are experiencing a change from decreasing to expanding forest cover, understanding the association of extinction and immigration processes, as well as their interactions with the landscape dynamic, is a key factor to plan conservation and restoration actions in human-altered landscapes.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Mortgage securitization and its credit risk control

This dissertation concentrate on the mortgage securitization and its credit risk, which are criticized as the main causes of the financial crisis. From the point of the veiw of mortgage's evolution, the nature, structure and function of mortgage has been radically changed, yet the mortgage law did not give appropriate response to this market change. Meanwhile, the U.S legilslations facilitating the mortgage securitization also have rotten the legal foundations for mortgage market self-regulation and sustained development. In contrast, the EU covered bond system has kept financial stability for 200 years' time, and their statutory approach has been proved to be able to control the credit risk and incentive problems very well, in combination of market self-regulation and public regulation. So the future reform should be directed to strengthen the market's capacity of self-regulation and improve the public regulation. For the development of mortgage securitization in China, it is suggested to introduce the EU covered bond system for the reason of the equilibrium between funding efficiency and financial stability.

Credit-Default-Swaps and the 2008 Financial Crisis: An Inquiry into the Underlying Legal Origins and the European Union’s Regulatory Reforms

After the 2008 financial crisis, the financial innovation product Credit-Default-Swap (CDS) was widely blamed as the main cause of this crisis. CDS is one type of over-the-counter (OTC) traded derivatives. Before the crisis, the trading of CDS was very popular among the financial institutions. But meanwhile, excessive speculative CDSs transactions in a legal environment of scant regulation accumulated huge risks in the financial system. This dissertation is divided into three parts. In Part I, we discussed the primers of the CDSs and its market development, then we analyzed in detail the roles CDSs had played in this crisis based on economic studies. It is advanced that CDSs not just promoted the eruption of the crisis in 2007 but also exacerbated it in 2008. In part II, we asked ourselves what are the legal origins of this crisis in relation with CDSs, as we believe that financial instruments could only function, positive or negative, under certain legal institutional environment. After an in-depth inquiry, we observed that at least three traditional legal doctrines were eroded or circumvented by OTC derivatives. It is argued that the malfunction of these doctrines, on the one hand, facilitated the proliferation of speculative CDSs transactions; on the other hand, eroded the original risk-control legal mechanism. Therefore, the 2008 crisis could escalate rapidly into a global financial tsunami, which was out of control of the regulators. In Part III, we focused on the European Union’s regulatory reform towards the OTC derivatives market. In specific, EU introduced mandatory central counterparty clearing obligation for qualified OTC derivatives, and requires that all OTC derivatives shall be reported to a trade repository. It is observable that EU’s approach in re-regulating the derivatives market is different with the traditional administrative regulation, but aiming at constructing a new market infrastructure for OTC derivatives.

Essays on credit rating agencies and the assessment of sovereign risk

Sovereign ratings have only recently regained attention in the academic debate. This seems to be somewhat surprising against the background that their influence is well-known and that rating decisions have often been criticized in the past (as for example during the Asian crisis in the 90s). Sovereign ratings do not only assess the creditworthiness of governments: They are also included in the calculation of ratings for sub-sovereign issuers whereby their rating is usually restricted to the upper bound of the sovereign rating (sovereign ceiling). Earlier studies have also shown that the downgrade of a sovereign often leads to contagion effects on neighbor countries. This study focuses first on misleading incentives in the rating industry before chapter three summarizes the literature on the influence and determinants of sovereign ratings. The fourth chapter explores empirically how ratings respond to changes in sovereign debt across specific country groups. The fifth part focuses on single rating decisions of four selected rating agencies and investigates whether the timing of decisions gives reason for herding behavior. The final chapter presents a reform proposal for the future regulation of the rating industry in light of the aforementioned flaws.rn

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.