Relevant bibliographies by topics / DGE Model

Academic literature on the topic 'DGE Model'

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Published: 11 March 2023

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Journal articles on the topic "DGE Model"

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International Monetary Fund. "A Simple Dge Model for Inflation Targeting." IMF Working Papers 07, no. 197 (2007): 1. http://dx.doi.org/10.5089/9781451867619.001.

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DeBacker, Jason, Richard W. Evans, and Kerk L. Phillips. "Integrating Microsimulation Models of Tax Policy into a DGE Macroeconomic Model." Public Finance Review 47, no. 2 (February 5, 2019): 207–75. http://dx.doi.org/10.1177/1091142118816744.

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This article proposes a method for integrating individual effective tax rates and marginal tax rates computed from a microsimulation (partial equilibrium) model of tax policy with a dynamic general equilibrium model of tax policy that can provide macroeconomic analysis or dynamic scores of tax reforms. Our approach captures the rich heterogeneity, realistic demographics, and tax-code detail of the microsimulation model and allows this detail to inform a general equilibrium model with a relatively high degree of heterogeneity. In addition, we propose a functional form in which tax rates depend jointly on the levels of both capital income and labor income.
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Schaarschmidt, Stephanie, Axel Fischer, Ellen Zuther, and Dirk K. Hincha. "Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana." International Journal of Molecular Sciences 21, no. 5 (March 3, 2020): 1720. http://dx.doi.org/10.3390/ijms21051720.

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Quantification of gene expression is crucial to connect genome sequences with phenotypic and physiological data. RNA-Sequencing (RNA-Seq) has taken a prominent role in the study of transcriptomic reactions of plants to various environmental and genetic perturbations. However, comparative tests of different tools for RNA-Seq read mapping and quantification have been mainly performed on data from animals or humans, which necessarily neglect, for example, the large genetic variability among natural accessions within plant species. Here, we compared seven computational tools for their ability to map and quantify Illumina single-end reads from the Arabidopsis thaliana accessions Columbia-0 (Col-0) and N14. Between 92.4% and 99.5% of all reads were mapped to the reference genome or transcriptome and the raw count distributions obtained from the different mappers were highly correlated. Using the software DESeq2 to determine differential gene expression (DGE) between plants exposed to 20 °C or 4 °C from these read counts showed a large pairwise overlap between the mappers. Interestingly, when the commercial CLC software was used with its own DGE module instead of DESeq2, strongly diverging results were obtained. All tested mappers provided highly similar results for mapping Illumina reads of two polymorphic Arabidopsis accessions to the reference genome or transcriptome and for the determination of DGE when the same software was used for processing.
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Angarita, Belcy K., Rodolfo J. C. Cantet, Kaitlin E. Wurtz, Carly I. O’Malley, Janice M. Siegford, Catherine W. Ernst, Simon P. Turner, and Juan P. Steibel. "Estimation of indirect social genetic effects for skin lesion count in group-housed pigs by quantifying behavioral interactions1." Journal of Animal Science 97, no. 9 (September 2019): 3658–68. http://dx.doi.org/10.1093/jas/skz244.

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Abstract Mixing of pigs into new social groups commonly induces aggressive interactions that result in skin lesions on the body of the animals. The relationship between skin lesions and aggressive behavioral interactions in group-housed pigs can be analyzed within the framework of social genetic effects (SGE). This study incorporates the quantification of aggressive interactions between pairs of animals in the modeling of SGE for skin lesions in different regions of the body in growing pigs. The dataset included 792 pigs housed in 59 pens. Skin lesions in the anterior, central, and caudal regions of the body were counted 24 h after pig mixing. Animals were video-recorded for 9 h postmixing and trained observers recorded the type and duration of aggressive interactions between pairs of animals. The number of seconds that pairs of pigs spent engaged in reciprocal fights and unilateral attack behaviors were used to parametrize the intensity of social interactions (ISI). Three types of models were fitted: direct genetic additive model (DGE), traditional social genetic effect model (TSGE) assuming uniform interactions between dyads, and an intensity-based social genetic effect model (ISGE) that used ISI to parameterize SGE. All models included fixed effects of sex, replicate, lesion scorer, weight at mixing, premixing lesion count, and the total time that the animal spent engaged in aggressive interactions (reciprocal fights and unilateral attack behaviors) as a covariate; a random effect of pen; and a random direct genetic effect. The ISGE models recovered more direct genetic variance than DGE and TSGE, and the estimated heritabilities (h^D2) were highest for all traits (P < 0.01) for the ISGE with ISI parametrized with unilateral attack behavior. The TSGE produced estimates that did not differ significantly from DGE (P > 0.5). Incorporating the ISI into ISGE, even in a small dataset, allowed separate estimation of the genetic parameters for direct and SGE, as well as the genetic correlation between direct and SGE (r^ds), which was positive for all lesion traits. The estimates from ISGE suggest that if behavioral observations are available, selection incorporating SGE may reduce the consequences of aggressive behaviors after mixing pigs.
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Lane, Sarah M., Alastair J. Wilson, and Mark Briffa. "Analysis of direct and indirect genetic effects in fighting sea anemones." Behavioral Ecology 31, no. 2 (January 10, 2020): 540–47. http://dx.doi.org/10.1093/beheco/arz217.

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Abstract Theoretical models of animal contests such as the Hawk-Dove game predict that variation in fighting behavior will persist due to mixed evolutionarily stable strategies (ESS) under certain conditions. However, the genetic basis for this variation is poorly understood and a mixed ESS for fighting can be interpreted in more than one way. Specifically, we do not know whether variation in aggression within a population arises from among-individual differences in fixed strategy (determined by an individual’s genotype—direct genetic effects [DGEs]), or from within-individual variation in strategy across contests. Furthermore, as suggested by developments of the original Hawk-Dove model, within-individual variation in strategy may be dependent on the phenotype and thus genotype of the opponent (indirect genetic effects—IGEs). Here we test for the effect of DGEs and IGEs during fights in the beadlet sea anemone Actinia equina. By exploiting the unusual reproductive system of sea anemones, combined with new molecular data, we investigate the role of both additive (DGE + IGE) and non-additive (DGE × IGE) genetic effects on fighting parameters, the latter of which have been hypothesized but never tested for explicitly. We find evidence for heritable variation in fighting ability and that fight duration increases with relatedness. Fighting success is influenced additively by DGEs and IGEs but we found no evidence for non-additive IGEs. These results indicate that variation in fighting behavior is driven by additive indirect genetic effects (DGE + IGE), and support a core assumption of contest theory that strategies are fixed by DGEs.
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Zhou, Sheng, Xin Wang, Jiajun Bu, Martin Ester, Pinggang Yu, Jiawei Chen, Qihao Shi, and Can Wang. "DGE: Deep Generative Network Embedding Based on Commonality and Individuality." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6949–56. http://dx.doi.org/10.1609/aaai.v34i04.6178.

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Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.
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Barajas, Belcy Karine Angarita, Rodolfo Cantet, Kaitlin E. Wurtz, Carly O’Malley, Janice Siegford, Catherine Ernst, and Juan P. Steibel. "62 Improved estimation of indirect social genetic effects in group-housed pigs by quantifying behavioral interactions." Journal of Animal Science 97, Supplement_2 (July 2019): 34. http://dx.doi.org/10.1093/jas/skz122.063.

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Abstract Traditional social genetic effects modeling assumes uniform intensity of interaction between group members. Tree breeders proposed relaxing this assumption by incorporating estimates of intensity of competition between pairs of individuals. Here, we incorporated the quantification of aggressive interactions between pairs of animals in the estimation of indirect social genetic effects on skin lesions in the anterior part of the body in growing pigs. The data consisted of 491 pigs (215 barrows and 276 gilts, mean of 66 ±5 days of age). Animals were housed in 37 pens (11 to 15 pigs by pen) over 7 replicates. Trained scorers counted the number of skin lesions immediately before and 24 hours after mixing pigs. Animals were video-recorded for 9 hours post mixing and trained observers quantified the type and duration of aggressive interactions between pairs of pigs. The number of skin lesions in the frontal part of the body 24 hours post-mixing was the response variable and the number of seconds that pairs of animals spent engaged in reciprocal fights was used to quantify the intensity of interaction. We compared three different models: A direct genetic additive model (DGE), a traditional social genetic effect model (TSGE) assuming uniform interactions, and an improved social genetic effect model (ISGE) that used intensity of interaction to parameterize social genetic effects. All models included fixed effects of sex, replicate, lesion scorer, initial weight and pre-mixing lesion count; a random effect of pen; and a random direct genetic effect. The model ISGE recovered the most variance (smallest σe2) and resulted in the highest estimated h2 (P < 0.005). The model TSGE produced estimates that did not differ significantly from DGE (P = 1). Contrarily, incorporating the intensity of interaction into the modeling of ISGE allowed direct and indirect genetic effects to be estimated separately, even in a small dataset.
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Xiao, Kang-Wen, Zhi-Bo Liu, Zi-Hang Zeng, Fei-Fei Yan, Ling-Fei Xiao, Jia-Li Li, and Lin Cai. "Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma." Journal of Oncology 2021 (June 2, 2021): 1–18. http://dx.doi.org/10.1155/2021/9967954.

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Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.
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Kong, Lingkai, and Yunxin Chang. "THE IMPULSE RESPONSES OF CAPITAL, CONSUMPTION, AND PRICES IN THE STOCHASTIC RAMSEY MODEL WITH AN INFINITE TIME HORIZON EMPLOYING VALUE FUNCTION ITERATION AND LINEAR INTERPOLATION." JOURNAL OF QUANTITATIVE FINANCE AND ECONOMICS 4, no. 2 (2022): 161–78. http://dx.doi.org/10.47509/jqfe.2022.v04i02.01.

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In this paper, we use value function iteration and linear interpolation to solve an example of a stochastic, infinite-horizon Ramsey model, which is one kind of dynamic general equilibrium (DGE) model. To obtain an approximation of the policy function’s solution, we pick a grid of capital investments for each period, set the starting value of the value function, build an iterative loop to compute the new value using interpolation, and establish the selection criteria and stop conditions. We model transient shocks to total factor productivity (TFP) and capture the impulsive reactions of consumption, capital, and factor pricing, with the algorithm anticipated to converge in “x” iterations. This concept is helpful and inspiring for assessing financial risk avoidance and dealing with and better comprehending potential shocks.
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Johri, Alok, and Md Mahbubur Rahman. "The Rise and Fall of India’s Relative Investment Price: A Tale of Policy Error and Reform." American Economic Journal: Macroeconomics 14, no. 1 (January 1, 2022): 146–78. http://dx.doi.org/10.1257/mac.20180411.

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India’s relative price of investment rose 44 percent from 1981 to 1991 and fell 26 percent from 1991 to 2006. We build a simple DGE model, calibrated to Indian data, in order to explore the impact of capital import substitution policies and their reform post-1991 in accounting for this rise and fall. Our model delivers a 23 percent rise before reform and a 31 percent fall thereafter. GDP per effective labor was 3 percent lower in 1991 compared to 1981 due to import restrictions on capital goods. Their removal, and a 71 percentage point reduction in tariff rates, raised GDP per effective labor permanently by 20 percent. (JEL E22, E23, F13, O11, O16, O19)
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Dissertations / Theses on the topic "DGE Model"

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Průchová, Anna. "Makroekonomická analýza pomocí DSGE modelů." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-124606.

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Dynamic stochastic general equilibrium models are derived from microeconomic principles and they retain the hypothesis of rational expectations under policy changes. Thus they are resistant to the Lucas critique. The DSGE model has become associated with new Keynesian thinking. The basic New Keynesian model is studied in this thesis. The three equations of this model are dynamic IS curve, Phillips-curve and monetary policy rule. Blanchard and Kahn's approach is introduced as the solution strategy for linearized model. Two methods for evaluating DSGE models are presented -- calibration and Bayesian estimation. Calibrated parametres are used to fit the model to Czech economy. The results of numeric experiments are compared with empricial data from Czech republic. DSGE model's suitability for monetary policy analysis is evaluated.
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Sjöberg, Johan. "Optimal Control and Model Reduction of Nonlinear DAE Models." Doctoral thesis, Linköpings universitet, Reglerteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11345.

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In this thesis, different topics for models that consist of both differential and algebraic equations are studied. The interest in such models, denoted DAE models, have increased substantially during the last years. One of the major reasons is that several modern object-oriented modeling tools used to model large physical systems yield models in this form. The DAE models will, at least locally, be assumed to be described by a decoupled set of ordinary differential equations and purely algebraic equations. In theory, this assumption is not very restrictive because index reduction techniques can be used to rewrite rather general DAE models to satisfy this assumption. One of the topics considered in this thesis is optimal feedback control. For state-space models, it is well-known that the Hamilton-Jacobi-Bellman equation (HJB) can be used to calculate the optimal solution. For DAE models, a similar result exists where a Hamilton-Jacobi-Bellman-like equation is solved. This equation has an extra term in order to incorporate the algebraic equations, and it is investigated how the extra term must be chosen in order to obtain the same solution from the different equations. A problem when using the HJB to find the optimal feedback law is that it involves solving a nonlinear partial differential equation. Often, this equation cannot be solved explicitly. An easier problem is to compute a locally optimal feedback law. For analytic nonlinear time-invariant state-space models, this problem was solved in the 1960's, and in the 1970's the time-varying case was solved as well. In both cases, the optimal solution is described by convergent power series. In this thesis, both of these results are extended to analytic DAE models. Usually, the power series solution of the optimal feedback control problem consists of an infinite number of terms. In practice, an approximation with a finite number of terms is used. A problem is that for certain problems, the region in which the approximate solution is accurate may be small. Therefore, another parametrization of the optimal solution, namely rational functions, is studied. It is shown that for some problems, this parametrization gives a substantially better result than the power series approximation in terms of approximating the optimal cost over a larger region. A problem with the power series method is that the computational complexity grows rapidly both in the number of states and in the order of approximation. However, for DAE models where the underlying state-space model is control-affine, the computations can be simplified. Therefore, conditions under which this property holds are derived. Another major topic considered is how to include stochastic processes in nonlinear DAE models. Stochastic processes are used to model uncertainties and noise in physical processes, and are often an important part in for example state estimation. Therefore, conditions are presented under which noise can be introduced in a DAE model such that it becomes well-posed. For well-posed models, it is then discussed how particle filters can be implemented for estimating the time-varying variables in the model. The final topic in the thesis is model reduction of nonlinear DAE models. The objective with model reduction is to reduce the number of states, while not affecting the input-output behavior too much. Three different approaches are studied, namely balanced truncation, balanced truncation using minimization of the co-observability function and balanced residualization. To compute the reduced model for the different approaches, a method originally derived for nonlinear state-space models is extended to DAE models.
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Gendron, Debbie. "Model stability under a policy shift : are DSGE models really structural?" Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24214/24214.pdf.

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PACCAGNINI, ALESSIA. "Model validation in the DSGE approach." Doctoral thesis, Universita' Bocconi Milano, 2009. http://hdl.handle.net/10281/13792.

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The purpose of this thesis is to discuss the introduction and the implementation of the idea of model validation, especially in the use of Dynamic Stochastic General Equilibrium (DSGE) models. In this discussion, the mixture models are presented as the recent econometrics tool used in model validation. Two examples of DSGE models are illustrated in order to introduce two problems: omitted variables within the statistical identification problem and the finite-order representation by a Vector Autoregressive (VAR) of a DSGE model. The paper concludes the review considering some pointers for the future research and for the further developments of the use of mixture models for model validation.
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Sjöberg, Johan. "Optimal control and model reduction of nonlinear DAE models /." Linköping : Department of Electrical Engineering, Linköping University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11345.

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Zhu, Chuanqi. "Essays on macroeconometrics." Thesis, Boston College, 2013. http://hdl.handle.net/2345/bc-ir:104398.

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Thesis advisor: Zhijie Xiao
This dissertation contains three chapters in theoretical Macroeconometrics and applied Macroeconometrics. This first chapter addresses the issues related to the estimation, testing and computation of ordered structural breaks in multivariate linear regressions. Unlike common breaks, ordered structural breaks are those breaks that are related across equations but not necessarily occurring at the same dates. A likelihood ratio test assuming normal errors is proposed in this chapter in order to detect the ordered structural breaks in multivariate linear regressions. The estimation of ordered structural breaks uses quasi-maximum likelihood and adopts the efficient algorithm of Bai and Perron (2003). I also provide results about the consistency and rate of convergence when searching for ordered structural breaks. Finally, these methods are applied to one empirical example: the mean growth rate of output in three European countries and United States. This second chapter focuses on the parameter stability of dynamic stochastic general equilibrium (DSGE) models. To this end, I solve and estimate a representative New Keynesian model using both linear and nonlinear methods. I first examine how nonlinearities affect the parameter stability of the New Keynesian model. The results show that parameter instabilities still exist even using nonlinear solutions, and also highlight differences between two nonlinear solution methods: perturbation method and projection method. In addition, I propose a sequential procedure for searching for multiple structural breaks in nonlinear models, and apply it to the New Keynesian model. Two common structural breaks among these estimated parameters are identified for all the five solutions considered in this chapter. One structural break is in the early 1970s, while another one locates around the middle 1990s. In the third chapter, we investigate changes in long run productivity growth in the United States. In particular, we approach productivity growth from a sectoral perspective, and decompose the whole economy into two broad sectors: investment goods-producing sector and consumption goods-producing sector. Although the evidence of changes in the aggregate productivity growth is far from obvious at conventional test size, we find evidence of structural breaks in the sectoral productivity growth using both growth accounting and DSGE model based measures. There are two structural breaks in investment goods-producing sector using growth accounting measures, which indicates that the era of investment and productivity boom in the middle 1990s may have ended before the Great Recession. In addition, our results show there is one structural break in consumption goods-producing sector around the 1970s and attribute the aggregate productivity slowdown at that time to consumption goods-producing sector. These results are broadly consistent with Ireland and Schuh (2008). Our results offer up with a modestly pessimistic outlook on future productivity growth and, therefore, potential output
Thesis (PhD) — Boston College, 2013
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Economics
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Ornellas, Raphael da Silva. "Interação entre as autoridades fiscal e monetária no Brasil." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/35594.

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O objetivo deste trabalho é estudar a interação entre as autoridades fiscal e monetária no Brasil, de forma a mensurar o nível de dominância fiscal existente na economia brasileira. Para alcançar este objetivo, utiliza-se um modelo de equilíbrio geral dinâmico e estocástico desenvolvido para uma economia com rigidez de preços e com tendência inflacionária, cujos parâmetros de interesses são estimados por inferência bayesiana. Conclui-se que o nível de dominância fiscal na economia brasileira é baixa, em patamar comparado ao da economia norte-americana e canadense. Este resultado tem impacto direto na condução de políticas que visam a redução da inflação, sugerindo que esta atividade deva passar pelo encolhimento das metas inflacionárias, que impactaria diretamente na expectativa dos agentes sobre a inflação futura.
The purpose of this dissertartion is to analyse the interaction between fiscal and monetary authorities in Brazil, in a way that we can be able to measure the level of fiscal dominance occurring in brazilian economy. To attain this purpose, we make use of a dynamic stochastic general equilibrium model with sticky prices and non-zero trend inflation, whose parameters are estimated by bayesian inference. We conclude that the level of the fiscal dominance in Brazil is low, in scale compared to american e canadian economies. This result has consequence in policy conduction that aims to decrease inflation, suggesting that may be necessary straiten the inflation target to reduce the inflation and affect the agent’s expectation about the future inflation.
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Gómez, Sánchez Pilar. "Analyzing the parallel applications’ I/O behavior impact on HPC systems." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/586177.

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Donat que el volum de dades generat per les aplicacions científiques creix i la pressió sobre el sistema d’E/S dels sistemes HPC també augmenta, es proposa un model de comportament d’E/S per les aplicacions cientifiques paral.leles de pas de missatges MPI (Message Passing Interface) amb l’objectiu d’analitzar l’impacte de les aplicacions en el sistema d’E/S. Analitzar les aplicacions les aplicacions paral.leles MPI a nivell POSIX-IO permet observar com es tracten les dades de l’aplicació en aquest nivell. En aquest treball de recerca es presenta: la definició del model PIOM-PX. la metodologia aplicada per extraure el model i l’eina PIOM-PX-Trace-Tool. Donat que PIOM-PX està basat en el concepte de E/S, es poden identificar les fases més significatives. Fases que tenen més influència que altres en el sistema d’E/S, provocant un coll d’ampolla o un rendiment pobre. L’anàlisis en base a les fases d’E/S permeten identificar, acotar i intentar reduir l’impacte d’aquestes fases sobre el sistema d’E/S. PIOM-PX forma part del model proposat PIOM que integra el model de comportament d’E/S a nivell de POSIX-IO (PIOM-PX) i el model de comportament d’E/S a nivell de MPI-IO (PIOM-MP, antic PAS2P-IO). El model proporciona la informació necessaria, per a que utilitzant programes sintètics programables es pugui replicar el comportament de l’aplicació en diferents sistemes. PIOM-PX-Trace-Tool permet interceptar instruccions de POSIX-IO utilitzades durant l’execució de l’aplicació. Els experiments realitzats s’han executat en varis sistemes HPC estandard i en la plataforma Cloud, on s’ha pogut comprovar la utilitat del model proposat, PIOM.
Dado que el volumen de datos generado por las aplicaciones científicas crece y la presión sobre el sistema de E/S de los sistemas HPC también aumenta, se propone un modelo de comportamiento de E/S para las aplicaciones científicas paralelas de paso de mensajes (MPI -Message Passing Interface-) con el objetivo de analizar el impacto de las aplicaciones en el sistema de E/S. Analizar las aplicaciones paralelas MPI a nivel POSIX-IO permite observar cómo se tratan los datos de la aplicación a ese nivel. En este trabajo de investigación se presenta: la definición del modelo PIOM-PX, la metodología aplicada para extraer dicho modelo y la herramienta PIOM-PX-Trace-Tool. Dado que PIOM-PX está basado en el concepto de fase de E/S, se pueden identificar las fases más significativas. Fases que tienen más influencia que otras en el sistema de E/S, que podrían provocar un cuello de botella o un rendimiento pobre. El análisis en base a las fases de E/S permite identificar, acotar e intentar reducir el impacto de esas fases sobre el sistema de E/S. PIOM-PX forma parte del modelo propuesto PIOM que integra el modelo de comportamiento de E/S a nivel de POSIX-IO (PIOM-PX) y el modelo de comportamiento de E/S a nivel de MPI-IO (PIOM-MP, antiguo PAS2P-IO). El modelo proporciona la información necesaria, para que utilizando programas sintéticos programables se pueda replicar el comportamiento de la aplicación en diferentes sistemas. PIOM-PX-Trace-Tool permite interceptar instrucciones de POSIX-IO utilizadas durante la ejecución de la aplicación. Los experimentos realizados se han ejecutado en varios sistemas HPC estándar y en la plataforma Cloud, donde se ha podido comprobar la utilidad del modelo propuesto, PIOM.
The volume of data generated by scientific applications grows and the pressure on the I/O system of HPC systems also increases. For this reason, an I/O behavior model is proposed for scientific MPI (Message Passing Interface) parallel applications. The goal is to analyze the applications’ impact on the I/O system. Analyzing the MPI parallel applications at POSIX-IO level allows observing how the application’s data are treated at that level. In this research work, the following is presented: the I/O behavior model definition at POSIX-IO level (PIOM-PX model definition), the methodology applied to extract this model and the PIOM-PX-Trace-Tool. As PIOM-PX is based on the I/O phase concept, it can identify the more significant phases. Phases that have more influence than others in the I/O system and they could provoke a bottleneck or a poor performance. Analysis based on I/O phases allows identifying, delimiting, and trying to reduce each phase’s impact on the I/O system. PIOM-PX is part of proposed model PIOM. PIOM integrates the I/O behavior model at POSIX-IO level (PIOMPX) and the I/O behavior model at MPI-IO level (PIOM-MP, formerly known as PAS2P-IO). The model provides the information necessary to replicate an application’s behavior in different systems using synthetic programmables programs. PIOM-PX-Trace-Tool allows interception of POSIX-IO instructions used during the application execution. The experiments carried out are executed in several standar HPC systems and the Cloud platform, where it is able to test the utility of the proposed model PIOM.
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Taveira, Marília Angelo. "Análise do papel da política macroprudencial e sua inserção em um modelo DSGE." reponame:Repositório Institucional do FGV, 2012. http://hdl.handle.net/10438/10458.

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Este estudo tem dois objetivos principais. O primeiro, discutir o propósito da popularização das políticas macroprudenciais no pós-crise – que surgiram como uma das soluções para a complexa relação entre estabilidade de preços e estabilidade financeira – suas vantagens em relação à abordagem anteriormente predominante – as políticas microprudenciais – e formas de interação com a tradicional política monetária. O segundo grande objetivo reproduzir um modelo da geração novo-keynesiana que contempla um sistema bancário e características que permitem replicar a condução de uma política macroprudencial (colaterais, depósitos compulsórios, requerimentos mínimos de capital) a fim de analisar a resposta de variáveis macroeconômicas a mudanças nestes parâmetros.
This study has two main goals. The first one is to discuss the popularization of macroprudential policies in the after crisis, as a solution for the complex linkage between financial stability and price stability, its benefits compared to the previous approach – the microprudential regulation – and the interaction between macroprudential and conventional monetary policies. The second main goal is to simulate a DSGE model with a banking system and subject to reserve requirements and collateral requirements that allow one to assess the effects of macroprudential tools utilization over macroeconomic variables.
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Štork, Zbyněk. "Term Structure of Interest Rates: Macro-Finance Approach." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-125158.

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Thesis focus on derivation of macro-finance model for analysis of yield curve and its dynamics using macroeconomic factors. Underlying model is based on basic Dynamic Stochastic General Equilibrium DSGE approach that stems from Real Business Cycle theory and New Keynesian Macroeconomics. The model includes four main building blocks: households, firms, government and central bank. Log-linearized solution of the model serves as an input for derivation of yield curve and its main determinants -- pricing kernel, price of risk and affine term structure of interest rates -- based on no-arbitrage assumption. The Thesis shows a possible way of consistent derivation of structural macro-finance model, with reasonable computational burden that allows for time varying term premia. A simple VAR model, widely used in macro-finance literature, serves as a benchmark. The paper also presents a brief comparison and shows an ability of both models to fit an average yield curve observed from the data. Lastly, the importance of term structure analysis is demonstrated using case of Central Bank deciding about policy rate and Government conducting debt management.
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Books on the topic "DGE Model"

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Benes, Jaromir. A simple DGE model for inflation targeting. [Washington, D.C.]: International Monetary Fund, Middle East and Central Asia Dept., 2007.

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Moser, Barry Kurt. Linear models: A mean model approach. San Diego: Academic Press, 1996.

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Borowiak, Dale S. Model discrimination for nonlinear regression models. New York: M. Dekker, 1989.

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Moran, Kevin. Estimated DGE models and forecasting accuracy: A preliminary investigation with Canadian data. Ottawa, Ont: Bank of Canada, 2002.

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Moran, Kevin. Estimated DGE models and forecasting accuracy: A preliminary investigation with Canadian data. [Ottawa]: Bank of Canada, 2002.

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Schill, Alexander, ed. DCE — The OSF Distributed Computing Environment Client/Server Model and Beyond. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57306-2.

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Lozowick, Lee. Le courage d'e duquer. [Gordes]: Le Relie, 2001.

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International DCE Workshop (1993 Karlsruhe, Germany). DCE--the OSF distributed computing environment: Client/server model and beyond : International DCE Workshop, Karlsruhe, Germany, October 7-8, 1993 : proceedings. Berlin: Springer-Verlag, 1993.

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Lennard, Diane. Coaching models: A cultural perspective a guide to model development for practitioners and students of coaching. New York: Routledge, 2010.

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Coaching models: A cultural perspective a guide to model development for practitioners and students of coaching. New York: Routledge, 2010.

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Book chapters on the topic "DGE Model"

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Tavasli, Serkan, and Bülent Jens Erwerle. "Das STEB-Modell: Modul 4 – die Umsetzung." In Veränderungsprojekte erfolgreich umsetzen, 201–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57701-1_8.

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Gong, Cheng, Xavier Parisot, and Detlef Reis. "Die Evolution der Digitalen Transformation." In Schwerpunkt Business Model Innovation, 281–316. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-36634-6_11.

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Lehmann, Christine. "Die Prinzessin." In Das Modell Clarissa, 14–19. Stuttgart: J.B. Metzler, 1991. http://dx.doi.org/10.1007/978-3-476-03352-9_2.

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Lehmann, Christine. "Die Waise." In Das Modell Clarissa, 39–51. Stuttgart: J.B. Metzler, 1991. http://dx.doi.org/10.1007/978-3-476-03352-9_4.

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Lehmann, Christine. "Die Dämonin." In Das Modell Clarissa, 74–83. Stuttgart: J.B. Metzler, 1991. http://dx.doi.org/10.1007/978-3-476-03352-9_7.

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Lehmann, Christine. "Die Imaginierte." In Das Modell Clarissa, 84–97. Stuttgart: J.B. Metzler, 1991. http://dx.doi.org/10.1007/978-3-476-03352-9_8.

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Balasubramanian, Ranjani, Carlo Vezzoli, Fabrizio Ceschin, Jacob Matthew, Abhijit Sinha, and Christoph Neusiedl. "Integrating S.PSS and DE." In Lecture Notes in Mechanical Engineering, 51–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66300-1_3.

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AbstractThe combination of Sustainable Product-Service Systems (S.PSS) and Distributed Economies (DE) has been considered as a promising mode of developing sustainability through regional resilience and by empowering a shift to a more localized economic model. This chapter examines case studies from around the world that use combinations of S.PSS and DE to varying degrees and in different industry sectors to analyse the opportunities that it presents. The chapter then goes on to provide a framework for categorizing various models of S.PSS and DE combinations and ends with an analysis of the barriers to implement such models.
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Priewe, Jan. "Die ökonomische Leistungsfähigkeit der deutschen Volkswirtschaft in den 90er Jahren." In Modell Deutschland — Modell Europa, 57–83. Wiesbaden: VS Verlag für Sozialwissenschaften, 1998. http://dx.doi.org/10.1007/978-3-322-93287-7_3.

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Özcan, Leon, Marvin Drewel, Christian Koldewey, and Roman Dumitrescu. "Strategische Planung des Einstiegs in die Plattformökonomie." In Schwerpunkt Business Model Innovation, 147–69. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-36634-6_7.

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Fusaro, Francesco. "Stellen Sie sich vor, die Digitalisierung funktioniert!" In Schwerpunkt Business Model Innovation, 569–612. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-36634-6_22.

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Conference papers on the topic "DGE Model"

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Yousefian, Sajjad, Gilles Bourque, Sandeep Jella, Philippe Versailles, and Rory F. D. Monaghan. "A Stochastic and Bayesian Inference Toolchain for Uncertainty and Risk Quantification of Rare Autoignition Events in DLE Premixers." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83667.

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Abstract Quantification of aleatoric uncertainties due to the inherent variabilities in operating conditions and fuel composition is essential for designing and improving premixers in dry low-emissions (DLE) combustion systems. Advanced stochastic simulation tools require a large number of evaluations in order to perform this type of uncertainty quantification (UQ) analysis. This task is computationally prohibitive using high-fidelity computational fluid dynamic (CFD) approaches such as large eddy simulation (LES). In this paper, we describe a novel and computationally-efficient toolchain for stochastic modelling using minimal input from LES, to perform uncertainty and risk quantification of a DLE system. More specially, high-fidelity LES, chemical reactor network (CRN) model, beta mixture model, Bayesian inference and sequential Monte Carlo (SMC) are integrated into the toolchain. The methodology is applied to a practical premixer of low-emission combustion system with dimethyl ether (DME)/methane-air mixtures to simulate autoignition events at different engine conditions. First, the benchmark premixer is simulated using a set of LESs for a methane/air mixture at elevated pressure and temperature conditions. A partitioning approach is employed to generate a set of deterministic chemical reactor network (CRN) models from LES results. These CRN models are then solved at the volume-average conditions and validated by LES results. A mixture modelling approach using the expectation-method of moment (EMM) is carried out to generate a set of beta mixture models and characterise uncertainties for LES-predicted temperature distributions. These beta mixture models and a normal distribution for DME volume fraction are used to simulate a set of stochastic CRN models. The Bayesian inference approach through Sequential Monte Carlo (SMC) method is then implemented on the results of temperature distributions from stochastic CRN models to simulate the probability of autoignition in the benchmark premixer. The results present a very satisfactory performance for the stochastic toolchain to compute the autoignition propensity for a few events with a particular combination of inlet temperature and DME volume fraction. Characterisation of these rare events is computationally prohibitive in the conventional deterministic methods such as high-fidelity LES.
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Govindarajulu, Naveen Sundar, and Selmer Bringsjord. "On Automating the Doctrine of Double Effect." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/658.

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The doctrine of double effect (DDE) is a long-studied ethical principle that governs when actions that have both positive and negative effects are to be allowed. The goal in this paper is to automate DDE. We briefly present DDE, and use a first-order modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine. We present formalizations of increasingly stronger versions of the principle, including what is known as the doctrine of triple effect. We then use our framework to simulate successfully scenarios that have been used to test the presence of the principle in human subjects. Our framework can be used in two different modes. One can use it to build DDE-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is DDE-complaint, by applying a DDE layer on an existing system or model. For the latter mode, the underlying AI system can be built using any architecture (planners, deep neural networks, bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes a few parameters in its model, such verification is possible. The role of the DDE layer here is akin to a (dynamic or static) software verifier that examines existing software modules. Finally, we end by sketching initial work on how one can apply our DDE layer to the STRIPS-style planning model, and to a modified POMDP model. This is preliminary work to illustrate the feasibility of the second mode, and we hope that our initial sketches can be useful for other researchers in incorporating DDE in their own frameworks.
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Thatte, Azam, and Voramon Dheeradhada. "Coupled Physics Performance Predictions and Risk Assessment for Dry Gas Seal Operating in MW-Scale Supercritical CO2 Turbine." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-57670.

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U.S. Department of Energy (DOE) has recently sponsored research programs to develop megawatt scale supercritical CO2 (sCO2) turbine for use in concentrated solar power (CSP) and fossil based applications. To achieve the CSP goal of power at $0.06/kW-hr LCOE and energy conversion efficiency > 50%, the sCO2 turbine relies critically on extremely low leakage film riding seals like dry gas seal (DGS). Although DGS technology has been used in other applications before. making it successful for stringent conditions of an sCO2 turbo-expander is challenging. This paper presents results from a multi-scale coupled physics model that predicts the performance of DGS under a typical sCO2 turbine mission cycle and addresses some of the risks specific to operation in sCO2. Real gas equations of state are incorporated in the models to capture large discontinuities in fluid properties close to the critical point. A novel experimental setup is developed to observe and characterize transition of CO2 through liquid-vapor and supercritical phases. Coupled fluid-structure-thermal interaction model investigates the effect of aerodynamic and thermal perturbations on the structural and rotordynamic instabilities. Dynamic instabilities arising from sonic transition in thin sCO2 film of DGS pose additional challenges while the large surface roughness changes due to sCO2 corrosion warrant further design considerations. Effectiveness of features like spiral grooves in converting fluid momentum into pressure rise in the thin film and also in achieving local flow reversals is investigated. Effect of various design features on the optimal performance is quantified and insights for a successful DGS operation in a sCO2 turbomachine are provided.
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NG, KAH FEE, and SHAO CHIN CINDY NG. "THE DGP MODEL REVISITED." In Conference in Honour of the 90th Birthday of Freeman Dyson. WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814590112_0033.

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Clark, Peter P., and Carmiña Londoño. "Raytracing Models for Diffractive Optical Elements." In Difraction Optics: Design, Fabrication, and Applications. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/do.1992.ma1.

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Optical designers currently have several ways to model surface relief diffractive optical elements (DOE's) on the computer. We are aware of three distinct techniques: the phase model1,2, the Sweatt model3 and the Southwell model4. We will compare them for accuracy, applicability and ease of use. These models must perform two functions: a) Accurately simulate the performance of the DOE. b) Be translatable into information that will allow the physical part to be produced.
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Gupta, G., and W. Fritz. "Probabilistic model for residential load profiles and power quality improvement." In 2016 International Conference on the Domestic Use of Energy (DUE). IEEE, 2016. http://dx.doi.org/10.1109/due.2016.7466704.

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Heunis, S., and M. Dekenah. "A load profile prediction model for residential consumers in South Africa." In 2014 International Conference on the Domestic Use of Energy (DUE). IEEE, 2014. http://dx.doi.org/10.1109/due.2014.6827763.

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Gerdes, Kirk, and Randall Gemmen. "Porous Anode Model for Coal Syngas Fuelled SOFC: Combined Mass and Energy Transport Normal to Cell Plane." In ASME 2008 6th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2008. http://dx.doi.org/10.1115/fuelcell2008-65055.

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Solid oxide fuel cells are being developed for integrated gasification combined cycle hybrid power systems. It is therefore necessary to evaluate the coupled temperature and concentration profiles for SOFC anodes exposed to coal syngas. In this work the SOFC anode was treated as a porous composite of 50/50 (volume) Ni / YSZ. Porous transport was modeled using the dusty gas model (DGM) and included two pore reactions, namely water gas shift and steam reforming of methane. The thermal transport model considered heat exchange by radiation between the interconnect and SOFC surface, convective transfer from bulk gas flow over the anode, heat generation terms due to pore reactions, and heat generation terms at the electrolyte boundary due to electrochemical reactions, ohmic heating, and concentration polarization. Composition profiles throughout the porous anode were considered for the DGM alone and were compared to the DGM including energy (DGME). The cases examined were for current densities ranging from 0.000–0.750 A/cm2 and for pressures from 1–19 atm absolute. Simulation results predict that the average cell operating temperature will increase 10 to 60°C relative to the furnace wall with inclusion of the energy equations. However, the thermal gradients within the anode are small due to the good thermal conductivity of the Ni-based anode. The effect of inclusion of energy transport on the hydrogen concentration profile is mixed depending on the independent parameter considered, with relative insensitivity to changes in the current density, but modest sensitivity to changes in operating pressure. Consideration of the thermal transport is important for determination of the interaction of coal syngas trace species with the anode, but is less critical for material stability.
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Maduagwu, Uchechukwu A., and Viranjay M. Srivastava. "Bridge rectifier with Cylindrical Surrounding Double-Gate MOSFET: A model for better efficiency." In 2017 International Conference on the Domestic Use of Energy (DUE). IEEE, 2017. http://dx.doi.org/10.23919/due.2017.7931832.

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Herrera, Ramón, Sérgio del Campo, Marco Olivares, Joel Saavedra, and Nelson Videla. "Intermediate inflation on warped DGP model." In II COSMOSUR: COSMOLOGY AND GRAVITATION IN THE SOUTHERN CONE. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4913347.

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Reports on the topic "DGE Model"

1

Aruoba, S. Borağan, Luigi Bocola, and Frank Schorfheide. Assessing DSGE Model Nonlinearities. Cambridge, MA: National Bureau of Economic Research, December 2013. http://dx.doi.org/10.3386/w19693.

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Bonaldi-Varón, Jean Pietro, Andrés González-Gómez, Juan David Prada-Sarmiento, and Diego Arturo Rodríguez-Guzmán. Método numérico para la calibración de un modelo DSGE. Bogotá, Colombia: Banco de la República, January 2009. http://dx.doi.org/10.32468/be.548.

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Christiano, Lawrence, Martin Eichenbaum, and Mathias Trabandt. On DSGE Models. Cambridge, MA: National Bureau of Economic Research, July 2018. http://dx.doi.org/10.3386/w24811.

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Hassan, Tarek, and Thomas Mertens. Information Aggregation in a DSGE Model. Cambridge, MA: National Bureau of Economic Research, June 2014. http://dx.doi.org/10.3386/w20193.

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Fioravanti, Reinaldo, José Yitani, Ana Haro González, Rodolfo Gomes Benevenuto, Rafael Ribeiro Silveira, Diego Camargo Botassio, and Renato Alves Morato. Estruturação de Propostas de Investimento em Infraestrutura - Modelo de 5 Dimensões: Adaptação do "Five Case Model" para o Contexto Brasileiro. Banco Interamericano de Desenvolvimento, July 2022. http://dx.doi.org/10.18235/0004359.

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Um dos maiores desafios para a economia brasileira é se consolidar em uma rota de prosperidade e bem-estar sustentável no médio e longo prazo. No entanto, superar a restriço orçamentária imposta pela pandemia e o histórico de baixa efetividade de investimentos na esfera pública no é trivial. A superaço desse desafio envolve no apenas a melhoria do gasto público, mas principalmente o aumento da atratividade do capital privado para nossa carteira de projetos de infraestrutura. Levantamentos recentes do Ministério da Economia realizados a partir dos diversos planos setoriais de infraestrutura indicam a necessidade de um choque de investimento da ordem de R$ 8,6 trilhes até 2050. A previso é que esse montante seja necessário para destravar o aumento da produtividade da nossa economia, além de garantir a universalizaço de serviços essenciais de infraestrutura para a populaço brasileira. Nesse sentido, o ferramental apresentado neste Guia oferece diretrizes para que os projetos de investimento em infraestrutura sejam feitos de maneira mais objetiva, transparente e sistemática, auxiliando o dirigente público na tomada de decises e na melhoria da qualidade dos gastos.
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Méndez-Vizcaíno, Juan C., Alexander Guarín, César Anzola-Bravo, and Anderson Grajales-Olarte. Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia. Banco de la República, October 2021. http://dx.doi.org/10.32468/be.1178.

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Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent the prospective factors of risk through the probability distributions of shocks.
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Muñoz, Roberto, Sergio Hinojosa, Patricio Mansilla, Juan Luis Gómez Reino, and Gerardo Reyes-Tagle. Vieja infraestructura financia nueva infraestructura: Un modelo de crecimiento de generaciones traslapadas para reciclaje de activos públicos. Inter-American Development Bank, March 2021. http://dx.doi.org/10.18235/0003155.

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En el presente trabajo se modela la existencia de los llamados “activos reciclables” de infraestructura y se muestra cómo este tipo de activos contribuye al crecimiento económico. Entendemos los activos reciclables como aquellos que generan un flujo de ingresos por pago por uso, a través de la delegación contractual de dichos activos al sector privado para su operación y mantenimiento. El modelo propone: a) que la administración privada de los activos permite extender su vida útil, y b) que el reciclaje de activos puede contribuir a relajar la restricción de financiamiento del Estado, permitiendo monetizar flujos cuya existencia no era evidente, y ampliando así las posibilidades de financiamiento de nueva infraestructura. El modelo concluye que el reciclaje de activos contribuye a incrementar el crecimiento económico.
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Sigrin, Benjamin, Michael Gleason, Robert Preus, Ian Baring-Gould, and Robert Margolis. Distributed Generation Market Demand Model (dGen): Documentation. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1239054.

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Cai, Michael, Marco Del Negro, Edward Herbst, Ethan Matlin, Reca Sarfati, and Frank Schorfheide. Online Estimation of DSGE Models. Cambridge, MA: National Bureau of Economic Research, March 2020. http://dx.doi.org/10.3386/w26826.

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Fernández-Villaverde, Jesús. The Econometrics of DSGE Models. Cambridge, MA: National Bureau of Economic Research, January 2009. http://dx.doi.org/10.3386/w14677.

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