Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a posterior in which the likelihood is replaced by the censored likelihood; and the censored predictive likelihood, which is used for Bayesian Model Averaging. We perform extensive experiments involving simulated and empirical data. Our results show the ability of these new approaches to outperform the standard posterior and traditional Bayesian Model Averaging techniques in applications of Value-at-Risk prediction in GARCH models

Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training

Testing After Worked Example Study Does Not Enhance Delayed Problem-Solving Performance Compared to Restudy

Four experiments investigated whether the testing effect also applies to the acquisition of problem-solving skills from worked examples. Experiment 1 (n = 120) showed no beneficial effects of testing consisting of isomorphic problem solving or example recall on final test performance, which consisted of isomorphic problem solving, compared to continued study of isomorphic examples. Experiment 2 (n = 124) showed no beneficial effects of testing consisting of identical problem solving compared to restudying an identical example. Interestingly, participants who took both an immediate and a delayed final test outperformed those taking only a delayed test. This finding suggested that testing might become beneficial for retention but only after a certain level of schema acquisition has taken place through restudying several examples. However, experiment 2 had no control condition restudying examples instead of taking the immediate test. Experiment 3 (n = 129) included such a restudy condition, and there was no evidence that testing after studying four examples was more effective for

A Combining Forecasting Modeling and Its Application

Part 5: Modelling and SimulationInternational audienceThe supply chain coordination has abstracted more and more attention from industries and academics. This paper studies a Bayesian combination forecasting model to integrate multiple forecasting resources and coordinate forecasting process among partners in retail supply chain. The simulation results based on the retail sales data show the effectiveness of this Bayesian combination forecasting model to coordinate the collaborative forecasting process. This Bayesian combination forecasting model can improve demand forecasting accuracy of supply chain

Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo

We discuss Bayesian inferential procedures within the family of instrumental variables regression models and focus on two issues: existence conditions for posterior moments of the parameters of interest under a flat prior and the potential of Direct Monte Carlo (DMC) approaches for efficient evaluation of such possibly highly non-elliptical posteriors. We show that, for the general case of m endogenous variables under a flat prior, posterior moments of order r exist for the coefficients reflecting the endogenous regressors' effect on the dependent variable, if the number of instruments is greater than m +r, even though there is an issue of local non-identification that causes non-elliptical shapes of the posterior. This stresses the need for efficient Monte Carlo integration methods. We introduce an extension of DMC that incorporates an acceptance-rejection sampling step within DMC. This Acceptance-Rejection within Direct Monte Carlo (ARDMC) method has the attractive property that the generated random drawings are independent, which greatly helps the fast convergence of simulation results, and which facilitates the evaluation of the numerical accuracy. The speed of ARDMC can be easily further improved by making use of parallelized computation using multiple core machines or computer clusters. We note that ARDMC is an analogue to the well-known "Metropolis-Hastings within Gibbs" sampling in the sense that one 'more difficult' step is used within an 'easier' simulation method. We compare the ARDMC approach with the Gibbs sampler using simulated data and two empirical data sets, involving the settler mortality instrument of Acemoglu et al. (2001) and father's education's instrument used by Hoogerheide et al. (2012a). Even without making use of parallelized computation, an efficiency gain is observed both under strong and weak instruments, where the gain can be enormous in the latter case

Bayesian Mode Inference for Discrete Distributions in Economics and Finance

Detecting heterogeneity within a population is crucial in many economic and financial applications. Econometrically, this requires a credible determination of multimodality in a given data distribution. We propose a straightforward yet effective technique for mode inference in discrete data distributions which involves fitting a mixture of novel shifted-Poisson distributions. The credibility and utility of our proposed approach is demonstrated through empirical investigations on datasets pertaining to loan default risk and inflation expectations

Flexible Negative Binomial Mixtures for Credible Mode Inference in Heterogeneous Count Data from Finance, Economics and Bioinformatics

In several scientific fields, such as finance, economics and bioinformatics, important theoretical and practical issues exist involving multimodal and asymmetric count data distributions due to heterogeneity of the underlying population. For accurate approximation of such distributions we introduce a novel class of flexible mixtures consisting of shifted negative binomial distributions, which accommodates a wide range of shapes that are commonly seen in these data. We further introduce a convenient reparameterization which is more closely related to a moment interpretation and facilitates the specification of prior information and the Monte Carlo simulation of the posterior. This mixture process is estimated by the sparse finite mixture Markov chain Monte Carlo method since it can handle a flexible number of non-empty components. Given loan payment, inflation expectation and DNA count data, we find coherent evidence on number and location of modes, fat tails and implied uncertainty measures, in contrast to conflicting evidence obtained from well-known frequentist tests. The proposed methodology may lead to more accurate measures of uncertainty and risk which improves prediction and policy analysis using multimodal and asymmetric count data

On farm conservation of cassava in traditional communities of Jangada, Mato Grosso State, Brazil: ethnobotany and genetic. diversity

Local cassava varieties play an important role in food security and the autonomy of subsistence farmers. They can be important resources for breeding and conservation programs. We examined the genetic diversity of cassava through ethnobotanical knowledge and microsatellite markers to understand the dynamics of conservation and management of the varieties used local small-scale farmers of a rural quilombo (a slave-descendant community) in Mato Grosso, Brazil. To obtain ethnobotanical information, semi-structured interviews were applied to 10 family units who cultivated cassava. Each family cultivated from one to five varieties, with 2.3 ± 1.16 varieties/farmer, on average. Genetic analysis was was made of the 11 local varieties with microsatellite markers (12 loci). Despite low ethnobotanical diversity (H' = 2.05), high genetic diversity was found (Na = 6.75, HO = 0.92, HE = 0.75, on average) in these local varieties. These farmers, who derive their income mainly from cassava cultivation and flour production for the market, direct their variety choices to those that are most productive. Brava variety was the most frequent (found in eight family units) and was considered the most profitable for the production of flour Network analysis showed that propagule circulation and information occurs between the residents and also with other communities of the region, which are important sources of new varieties. Two farmers were identified as the most active in this network, showing potential as key elements for the circulation of propagating material. According to the cluster analysis using the genetic data, the most recently introduced varieties (Baixinha, Liberatona, Broto roxo, Mansa, Ramo branco, Carneiro and Cuiabana) are separated from those introduced a long time ago. The varieties pointed out as bitter by the farmers were also grouped together. The results showed the importance of traditional farmers in maintaining a high genetic diversity of manioc varieties, despite the directing of the choice of varieties to meet market needs

On farm conservation of cassava in traditional communities of Jangada, Mato Grosso State, Brazil: ethnobotany and genetic diversity.

Local cassava varieties play an important role in food security and the autonomy of subsistence farmers. They can be important resources for breeding and conservation programs. We examined the genetic diversity of cassava through ethnobotanical knowledge and microsatellite markers to understand the dynamics of conservation and management of the varieties used local small-scale farmers of a rural quilombo (a slave-descendant community) in Mato Grosso, Brazil. To obtain ethnobotanical information, semi-structured interviews were applied to 10 family units who cultivated cassava. Each family cultivated from one to five varieties, with 2.3 ± 1.16 varieties/farmer, on average. Genetic analysis was was made of the 11 local varieties with microsatellite markers (12 loci). Despite low ethnobotanical diversity (H' = 2.05), high genetic diversity was found (Na = 6.75, HO = 0.92, HE = 0.75, on average) in these local varieties. These farmers, who derive their income mainly from cassava cultivation and flour production for the market, direct their variety choices to those that are most productive. Brava variety was the most frequent (found in eight family units) and was considered the most profitable for the production of flour Network analysis showed that propagule circulation and information occurs between the residents and also with other communities of the region, which are important sources of new varieties. Two farmers were identified as the most active in this network, showing potential as key elements for the circulation of propagating material. According to the cluster analysis using the genetic data, the most recently introduced varieties (Baixinha, Liberatona, Broto roxo, Mansa, Ramo branco, Carneiro and Cuiabana) are separated from those introduced a long time ago. The varieties pointed out as bitter by the farmers were also grouped together. The results showed the importance of traditional farmers in maintaining a high genetic diversity of manioc varieties, despite the directing of the choice of varieties to meet market needs

Testing After Worked Example Study Does Not Enhance Delayed Problem-Solving Performance Compared to Restudy

Four experiments investigated whether the testing effect also applies to the acquisition of problem-solving skills from worked examples. Experiment 1 (n = 120) showed no beneficial effects of testing consisting of isomorphic problem solving or example recall on final test performance, which consisted of isomorphic problem solving, compared to continued study of isomorphic examples. Experiment 2 (n = 124) showed no beneficial effects of testing consisting of identical problem solving compared to restudying an identical example. Interestingly, participants who took both an immediate and a delayed final test outperformed those taking only a delayed test. This finding suggested that testing might become beneficial for retention but only after a certain level of schema acquisition has taken place through restudying several examples. However, experiment 2 had no control condition restudying examples instead of taking the immediate test. Experiment 3 (n = 129) included such a restudy condition, and there was no evidence that testing after studying four examples was more effective for final delayed test performance than restudying, regardless of whether restudied/tested problems were isomorphic or identical. Experiment 4 (n = 75) used a similar design as experiment 3 (i.e., testing/restudy after four examples), but with examples on a different topic and with a different participant population. Again, no evidence of a testing effect was found. Thus, across four experiments, with different types of initial tests, different problem-solving domains, and different participant populations, we found no evidence that testing enhanced delayed test performance compared to restudy. These findings suggest that the testing effect might not apply to acquiring problem-solving skills from worked examples