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on Computational Economics |
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Issue of 2018‒12‒03
ten papers chosen by |
| By: | Mbanda, Vandudzai; Bonga-Bonga, Lumengo |
| Abstract: | This paper analyses the economy-wide and distributional impacts of the increase in electricity tariff in the South African economy. Use is made of CGE-microsimulation to this end. The paper simulates both an actual price increase experienced in the economy and an increase linked to inflation. While the macro results show a negative impact on the economy in terms of a decrease in GDP, an increase in prices and an increase in unemployment, the micro results indicate that poverty, as measured by the headcount rate and poverty incidence curves, declines. This finding is similar to the finding by Boccanfuso et al. (2009) that when a significant proportion of households is connected to the grid, electricity sector reforms in the form of increasing tariffs to expand power generation can lead to poverty reduction. |
| Keywords: | CGE-microsimulation, electricity, South Africa |
| JEL: | C63 C68 Q43 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:90120&r=cmp |
| By: | Heinrich, F.; Appel, F. |
| Abstract: | This paper deals with the activity of non-agricultural investors in the German agricultural biogas production with an agent based approach. A literature review and two expert interviews are carried out for their characterization. An investor is hypothetically implemented in an east German case study region. The goal of the simulations with the agricultural structural model AgriPoliS is to determine its effects on other farms in the region and the region itself. The results show that the non-agricultural investor can run its business economically viable. The presence of this investor increases the rental prices in the region. This applies to both arable land and grassland. The results, however, suggest that an investor does not accelerate the structural change, because in this scenario more farms persist until the end of the simulation and especially smaller ones are economically better off. The investor has changed the cultivation patterns of the whole region: In general, an intensification of land use is observed as more energy crops are produced for the production of biogas substrates. On the other hand, the production of less intensive crops and cereals decreases. Regarding the use of grassland, the production of grass silage is increased at the expense of grazing. Acknowledgement : |
| Keywords: | Agribusiness |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae18:277171&r=cmp |
| By: | Stylianos Sakkas (European Commission - JRC); Andrea Conte (European Commission - JRC); Simone Salotti (European Commission - JRC) |
| Abstract: | The ESF is Europe's main instrument for supporting jobs, helping people get better jobs and ensuring fairer job opportunities for all EU citizens. The ESF includes 4 different thematic objectives aimed at promoting sustainable employment, social inclusion, education and the efficiency of the public administration. Policy simulations using the RHOMOLO dynamic CGE model show positive aggregate macro-economic effects of the ESF policy intervention. The ESF stimulates GDP and employment via increases in labour productivity, education and training, and additional demand-side effects. RHOMOLO is able to quantify the impact of the ESF in the EU as a whole as well as in each one of the 267 NUTS 2 EU regions. Less developed regions reap most of the benefits of ESF policy intervention. By 2030, the cumulative GDP impact of the ESF is larger than its cost, that is the ESF generates more than one euro for each euro spent in it. |
| Keywords: | rhomolo, growth, impact assessment, European social fund |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc113328&r=cmp |
| By: | Jian-Huang She; Dan Grecu |
| Abstract: | A new challenge to quantitative finance after the recent financial crisis is the study of credit valuation adjustment (CVA), which requires modeling of the future values of a portfolio. In this paper, following recent work in [Weinan E(2017), Han(2017)], we apply deep learning to attack this problem. The future values are parameterized by neural networks, and the parameters are then determined through optimization. Two concrete products are studied: Bermudan swaption and Mark-to-Market cross-currency swap. We obtain their expected positive/negative exposures, and further study the resulting functional form of future values. Such an approach represents a new framework for modeling XVA, and it also sheds new lights on other methods like American Monte Carlo. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.08726&r=cmp |
| By: | Ali Al-Aradi; Adolfo Correia; Danilo Naiff; Gabriel Jardim; Yuri Saporito |
| Abstract: | In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field games, etc. The main idea behind DGM is to represent the unknown function of interest using a deep neural network. A key feature of this approach is the fact that, unlike other commonly used numerical approaches such as finite difference methods, it is mesh-free. As such, it does not suffer (as much as other numerical methods) from the curse of dimensionality associated with highdimensional PDEs and PDE systems. The main goals of this paper are to elucidate the features, capabilities and limitations of DGM by analyzing aspects of its implementation for a number of different PDEs and PDE systems. Additionally, we present: (1) a brief overview of PDEs in quantitative finance along with numerical methods for solving them; (2) a brief overview of deep learning and, in particular, the notion of neural networks; (3) a discussion of the theoretical foundations of DGM with a focus on the justification of why this method is expected to perform well. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.08782&r=cmp |
| By: | Pu, M.; Zheng, F. |
| Abstract: | A stochastic simulation model, with adaptive expectation and multiplicative production shocks, is advocated to investigate the impacts of public grain buffer stocks in China. The effects of alternative public buffer stocks, with three price bands and nine storage capacity levels, are investigated from the perspectives of producer support, market stabilization, food security and social costs. The simulation results show that a narrow price band can improve policy performances and the storage capacity has marginal diminishing effects on above policy performances. For a given width of the price band, the symmetric price band could achieve policy goals at a relatively low cost. In practice, the Chinese government can lower floor price and restrict storage capacity in order to improve Minimum Price Procurement policies of rice and wheat. Acknowledgement : I would like to thank Yu Cheng from Development Research Center of the State Council of China, Xiaohua Yu from Georg-August-University of G ttingen, Chen Zhen for University of Georgia for comments on an earlier draft. This study is funded by the National Natural Science Foundation of China; under Grant [number 71673289]; Doctoral thesis scholarship of China Institute of Rural Studies, Tsinghua University [number 201525]. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae18:277510&r=cmp |
| By: | Cyril B\'en\'ezet; J\'er\'emie Bonnefoy; Jean-Fran\c{c}ois Chassagneux; Shuoqing Deng; Camilo Garcia Trillos; Lionel Len\^otre |
| Abstract: | In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distribution. For the pricing and hedging model, we chose a classical Black & Scholes model with a stochastic interest rate following a Hull & White model. The risk management model describing the evolution of the parameters of the pricing and hedging model is a Gaussian model. The new numerical method is compared with the traditional nested simulation approach. We review the convergence of both methods to estimate the risk indicators under consideration. Finally, we provide numerical results showing that the sparse grid approach is extremely competitive for models with moderate dimension. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.08706&r=cmp |
| By: | John Cuffe; Nathan Goldschlag |
| Abstract: | Integrating data from different sources has become a fundamental component of modern data analytics. Record linkage methods represent an important class of tools for accomplishing such integration. In the absence of common disambiguated identifiers, researchers often must resort to ''fuzzy" matching, which allows imprecision in the characteristics used to identify common entities across dfferent datasets. While the record linkage literature has identified numerous individually useful fuzzy matching techniques, there is little consensus on a way to integrate those techniques within a single framework. To this end, we introduce the Multiple Algorithm Matching for Better Analytics (MAMBA), an easy-to-use, flexible, scalable, and transparent software platform for business record linkage applications using Census microdata. MAMBA leverages multiple string comparators to assess the similarity of records using a machine learning algorithm to disambiguate matches. This software represents a transparent tool for researchers seeking to link external business data to the Census Business Register files. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:18-45&r=cmp |
| By: | Donal Smith; Christine Arriola; Caitlyn Carrico; Frank van Tongeren |
| Abstract: | This paper provides estimates of the potential trade effects of an exit of the United Kingdom (UK) from the European Union (EU) on exports and production at the sectoral level as well as GDP in the Netherlands. Owing to the high uncertainty regarding the final trade agreement between the negotiating parties, the choice has been made to assume a worst case outcome where trade relations between the United Kingdom and EU are governed by World Trade Organization (WTO) most favoured nation (MFN) rules. In doing so, it provides an upper bound estimate of the potential negative economic impact stemming from disruptions in trade. Any final trade agreement that would result in closer relationships between the United Kingdom and the EU could reduce this negative impact. Simulations using the METRO model suggest that from an increase in tariff and non-tariff measures (NTM’s) Dutch exports to the UK would fall by 17% and GDP declines by 0.7% in the medium term compared to baseline. This effect is from the trade channel absent any change in foreign direct investment (FDI) or productivity. The Dutch agri-food sector would experience a 22% fall in its UK exports. There are some sectors that gain from the export opportunities provided by Brexit, notably financial services and transport. |
| Keywords: | Brexit, computable general equilibrium model, European Union, international trade, METRO model, Netherlands, sectoral economic effects |
| JEL: | C10 C68 F13 F14 |
| Date: | 2018–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1518-en&r=cmp |
| By: | bailek, Alexandra |
| Abstract: | The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. The purpose of this research is to investigate the Economic Impact of service in hospitals and integrated delivery networks in the United States based on the readmission rates as the target variable. The data set includes information from 130 hospitals and integrated delivery networks in the United States from 1999 to 2008 to investigate significance of different factors in readmission rate. The dataset contains 101,766 patients’ encounters and 50 variables. The 30-day readmission rate is considered as an indicator of the quality of the health providers and is used as target variable in this project. Preliminary data analysis shows that age, admission type, discharge disposition etc. is correlated to the readmission rate and will be incorporated for further data analysis. Data analysis are performed on the diabetic patient dataset to develop a classification model to predict the likelihood for a discharged patient to be readmitted within 30 days. KNN, Naive Bayes and Logistic Regression algorithm were used to classify data and KNN appears to be the best approach to develop the model. Hospitalisations and drug prescriptions accounted for 50% and 20% of total readmission expenditure, respectively. Long term nursing home care after hospital admission cost an additional £46.4 million. With the ability to identify those patients who are more likely to be readmitted within 30 days, we can deploy the hospital resources more economically affordable while improving services. Based on the results it can be concluded that the direct cost of readmission rate for hospitals rose to £459 million in 2000 and nursing home costs rose to £111 million. Also, it can be perceived that a reduced length of hospital stay was associated with increased readmission rates for jaundice and dehydration. |
| Keywords: | Predictive Modeling, Re-admission, Simulation, Healthcare Service, Classification Modeling, Health care Quality Indicator |
| JEL: | A1 C1 C11 C3 C5 C53 O2 |
| Date: | 2018–10–11 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:89875&r=cmp |