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on Computational Economics |
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Issue of 2018‒12‒17
thirteen papers chosen by |
| By: | Francesco Di Comite (European Commission - DG ECFIN); Olga Diukanova (European Commission - JRC); Giovanni Mandras (European Commission - JRC); Javier Gómez Prieto (European Commission - JRC) |
| Abstract: | In this note we present the economic impact assessment of the European Regional Development Fund (ERDF) for thematic objectives TO1 "Research and innovation" and TO4 "Low-carbon economy" in the region of Apulia, Italy. The results are based on the RHOMOLO-IO demand multiplier analysis and on computer simulations with the multi-regional dynamic computable general equilibrium (CGE) model RHOMOLO. The former approach is used to calculate the sector-specific output multipliers following a demand-side shock, while the CGE simulations provide evidence of significant spillover effects spreading beyond the Apulian borders and stimulating economic growth in other regions with significant trade links with Apulia. Our results suggest that a €536 million increase in demand for the Manufacturing & Construction sector would entail an increase in total value added of €329 million, which is roughly 0.46% of the regional GDP. The RHOMOLO simulations show that the effects of policy interventions reach their peak in the last years of ERDF programming period (2020-2022), when the absorption of investment funding is at its full potential. In 2022, T01 and T04 investments of the ERDF increase Apulian by 0.2% above the baseline GDP projections. Given the high import intensity of the region, only one fourth of the overall effect is driven by the direct investments and three fourths depend on the productivity improvements achieved as a result of the specific policy design. This demonstrates that the implementation of policies that are effective in raising productivity ensures long term economic benefits even in the absence of continuous funding. |
| Keywords: | rhomolo, region, growth, smart specialisation, investment, impact assessment, modelling |
| JEL: | C54 C68 E62 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:termod:201804&r=cmp |
| By: | Patrizio Lecca (European Commission - JRC); Damiaan Persyn (European Commission - JRC); Andrea Conte (European Commission - JRC); Simone Salotti (European Commission - JRC) |
| Abstract: | The European Cohesion Policy 2007-2015 was implemented in challenging times due to the economic and financial crisis and widespread cuts to public investment. Cohesion Policy has been used to reach objectives related to employment, R&D, energy sustainability, education, and poverty and social exclusion. Policy simulations using the RHOMOLO dynamic CGE model show positive macro-economic effects of the policy at the EU level, with significant differences between less developed regions, transition regions, and more developed ones. Cohesion Policy funds mainly targeted the less developed regions which received 60% of the total investments, while transition regions and more developed ones received 24% and 16% of the total, respectively. RHOMOLO simulations estimate the long-run GDP impact of Cohesion Policy to be equal to +0.7% at the EU level, with peaks in some less developed regions above +5%. The cumulative multipliers associated with the Cohesion Policy funds are above one in most EU regions by 2030. |
| Keywords: | rhomolo, region, growth, cohesion policy, modelling, general equilibrium |
| JEL: | C54 C68 E62 R13 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc114185&r=cmp |
| By: | Martin Christensen (European Commission - JRC); Andrea Conte (European Commission - JRC); Filippo Di Pietro (European Commission - JRC); Patrizio Lecca (European Commission - JRC); Giovanni Mandras (European Commission - JRC); Simone Salotti (European Commission - JRC) |
| Abstract: | The Investment Plan for Europe aims at removing obstacles to investment, providing visibility and technical assistance to investment projects, and at making smarter use of financial resources. The Investment Plan is made up of three pillars: the European Fund for Strategic Investment (EFSI); the European Investment Advisory Hub and the European Investment Project Portal; and the removal of regulatory barriers to investment. Policy simulations using the RHOMOLO dynamic CGE model show positive aggregate macro-economic effects of the EFSI. This Policy Insight contains the result of an additional set of RHOMOLO simulations aimed at quantifying the macroeconomic impact of the legislative proposals contained in the third pillar of the Investment Plan. The EU GDP is expected to be 1.5% higher by 2030 thanks to the removal of barriers to investment in the areas of the Capital Markets Union, the Single Market Strategy, the Digital Single Market, and the Energy Union. This entails the creation of about one million of jobs across the entire EU. |
| Keywords: | rhomolo, region, growth, investment plan for europe, third pillar, capital markets union, single market strategy, energy union, digital single market, modelling |
| JEL: | C54 C68 E62 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc114088&r=cmp |
| By: | Greg Kirczenow; Masoud Hashemi; Ali Fathi; Matt Davison |
| Abstract: | This paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. We consider an example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to learn the features of the missing yield parameters from the historical data of the instruments traded in the chosen liquid market. The DAE algorithm is then challenged by two "point-in-time" inpainting algorithms taken from the image processing and computer vision domain. It is observed that, when tested on unobserved rate surfaces, the DAE algorithm exhibits superior performance thanks to the features it has learned from the historical shapes of yield curves. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.01102&r=cmp |
| By: | Suproteem K. Sarkar; Kojin Oshiba; Daniel Giebisch; Yaron Singer |
| Abstract: | Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, which especially poses a challenge to machine learning models used in financial services. We use the loan grade classification problem to explore how machine learning models are sensitive to small changes in user-reported data, using adversarial attacks documented in the literature and an original, domain-specific attack. Our work shows that a robust optimization algorithm can build models for financial services that are resistant to misclassification on perturbations. To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.11079&r=cmp |
| By: | Francesco Di Comite (European Commission - DG ECFIN); Patrizio Lecca (European Commission - JRC); Philippe Monfort (European Commission - DG REGIO); Damiaan Persyn (European Commission - JRC); Violeta Piculescu (European Commission - DG REGIO) |
| Abstract: | In this paper we assess the system-wide economic impact of the key financial instruments adopted by the European Union for the implementation of the regional policy: The Structural funds and The Cohesion Funds. We take a bottom-up approach by aggregating the 86 categories of expenditures defined in the Structural and Cohesion Funds into six main policy variables. The outcomes of the simulations are the results of a combination of demand-and-supply-side shocks that are implemented into the RHOMOLO spatial and dynamic general equilibrium model calibrated on a set of inter-regional Social Accounting Matrices for the year 2010. In our analysis we document the direct, indirect, and general equilibrium effects of the EU regional policy at the regional, national, and EU level. In the short-run, our simulation exercise suggests a pronounced variegate patters across EU regions. In the long-run, a more homogenous spatial distribution is detected. Moreover, we identify and quantify the interregional spillover effects arising from trade links and capital mobility. |
| Keywords: | rhomolo, region, growth, cohesion policy, modelling, general equilibrium |
| JEL: | C54 C68 E62 R13 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:termod:201803&r=cmp |
| By: | Vangelis Bacoyannis; Vacslav Glukhov; Tom Jin; Jonathan Kochems; Doo Re Song |
| Abstract: | We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.09549&r=cmp |
| By: | Zihao Zhang; Stefan Zohren; Stephen Roberts |
| Abstract: | We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data source representing trading and pricing movements. We demonstrate that uncertainty information derived from posterior predictive distributions can be utilised for position sizing, avoiding unnecessary trades and improving profits. Further, we test our models by using millions of observations across several instruments and markets from the London Stock Exchange. Our results suggest that those Bayesian techniques not only deliver uncertainty information that can be used for trading but also improve predictive performance as stochastic regularisers. To the best of our knowledge, we are the first to apply Bayesian networks to LOBs. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.10041&r=cmp |
| By: | Boulanger, P.; Dudu, H.; Ferrari, E.; M'Barek, R.; Philippidis, G. |
| Abstract: | This paper provides a model-based impact analysis of the Common Agricultural Policy (CAP) on the four main regional blocks in Sub-Saharan Africa (SSA). It uses the Modular Agricultural GeNeral Equilibrium Tool (MAGNET), a multi-region computable general equilibrium model. To provide a comprehensive analysis, other key EU policies, such as trade or GHG policies, are modelled as well. A thoroughly prepared reference scenario is contrasted with a counterfactual scenario, where the CAP is removed and ambitious trade agreements with non-African EU trade partners are implemented. Results provide interesting insights into the identification and quantification of - mainly indirect - effects of the CAP in SSA. Acknowledgement : |
| Keywords: | Agricultural and Food Policy |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae18:277427&r=cmp |
| By: | Letizia Mencarini; Delia Irazú Hernández-Farías; Mirko Lai; Viviana Patti; Emilio Sulis; Daniele Vignoli |
| Abstract: | his article explores opinions and semantic orientation around fertility and parenthood by scrutinizing filtered Italian Twitter data. We propose a novel methodological framework relying on Natural Language Processing techniques for text analysis, which is aimed at extracting sentiments from texts. A manual annotation for exploring sentiment and attitudes to fertility and parenthood was applied to Twitter data. The resulting set of tweets (corpus) was analysed through sentiment and emotion lexicons in order to highlight how affective language is used in this domain. It emerges that parents express a generally positive attitude towards their children and being and become parents, but quite negative sentiments on children’s future, politics and fertility and also parental behaviour. Exploiting geographical information from tweets, we find a significant correlation between the prevalence of positive sentiments about parenthood and macro-regional indicators for both life satisfaction and fertility levels. |
| Keywords: | sentiment analysis, social media, fertility, parenthood, subjective well-being, linguistic corpora |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:don:donwpa:117&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:18-46&r=cmp |
| By: | Ítalo Pedrosa (Federal University of Rio de Janeiro - UFJR (.)); Dany Lang (CEPN - Centre d'Economie de l'Université Paris Nord - UP13 - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In Minsky's Financial Instability Hypothesis (FIH), financial fragility of non-financial firms tends to increase endogenously over the cycle along with the macroeconomic leverage ratio. This analysis has been criticized for two main complementary reasons: firstly, it does not duly consider the aggregate pro-cyclicallity of profits; secondly, due to an overly aggregate analysis, some inferences about the relation between aggregate leverage and systemic fragility are potentially misleading. In this paper, we take these criticisms into account by building an agent-based stock-flow consistent model which integrates the real and financial sides of the economy in a fundamentally dynamic environment. We calibrate and simulate our model and show that the dynamics generated are in line with empirical evidence both at the micro and the macro levels. We create a financial fragility index and examine how systemic financial fragility relates to the aggregate leverage along the cycle. We show that our model yields both Min-skian regimes, in which the aggregate leverage increases along with investment, and Steindlian regimes, where investment brings leverage down. Our key findings are that the sensitivity of financial fragility to aggregate leverage is not as big as assumed in the literature; and that the distribution of profits amongst firms does matter for the stability of the system, both statically (immediately for financial fragility) and dynamically (because of the dynamics of leverage). |
| Keywords: | financial fragility,firms,leverage,cash flow,distribution |
| Date: | 2018–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cepnwp:hal-01937186&r=cmp |
| By: | Bart Smeulders |
| Abstract: | Kitamura and Stoye (2014) develop a nonparametric test for linear inequality constraints, when these are are represented as vertices of a polyhedron instead of its faces. They implement this test for an application to nonparametric tests of Random Utility Models. As they note in their paper, testing such models is computationally challenging. In this paper, we develop and implement more efficient algorithms, based on column generation, to carry out the test. These improved algorithms allow us to tackle larger datasets. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.01400&r=cmp |