nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒12‒24
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep neural networks algorithms for stochastic control problems on finite horizon, Part 2: numerical applications By Achref Bachouch; C\^ome Hur\'e; Nicolas Langren\'e; Huyen Pham
  2. Increase-Decrease Game under Imperfect Competition in Two-stage Zonal Power Markets Part II: Solution Algorithm By Sarfatia, M.; M., Hesamzadeha.; Holmberg, P.
  3. Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections By Chang, C-L.; McAleer, M.J.; Wong, W.-K.
  4. Calibrating rough volatility models: a convolutional neural network approach By Henry Stone
  5. Continuous Learning Augmented Investment Decisions By Daniel Philps; Tillman Weyde; Artur d'Avila Garcez; Roy Batchelor
  6. Immigration, Skill Acquisition and Fiscal Redistribution in a Search-Equilibrium Model By Ikhenaode, Bright Isaac
  7. Classifying Firms with Text Mining By Giacomo Caterini
  8. Assessing the regional socio-economic impact of the European R&I programme By Martin Christensen
  9. Long-run Economic, Budgetary and Fiscal Effects of Roma Integration Policies By Pavel Ciaian; Andrey Ivanov; d’Artis Kancs
  10. The Dominium Mundi Game and the Case for Artificial Intelligence in Economics and the Law By Rodríguez Arosemena, Nicolás
  11. The U.S. Syndicated Loan Market: Matching Data By Cohen, Gregory J.; Friedrichs, Melanie; Gupta, Kamran; Hayes, William; Lee, Seung Jung; Marsh, W. Blake; Mislang, Nathan; Shaton, Maya; Sicilian, Martin
  12. Flexible Retirement and Optimal Taxation By Ndiaye, Abdoulaye
  13. Bayesian Forecasting of Electoral Outcomes with new Parties' Competition By José García-Montalvo; Omiros Papaspiliopoulos; Timothée Stumpf-Fétizon
  14. Efficient Counterfactual Learning from Bandit Feedback By Yusuke Narita; Shota Yasui; Kohei Yata
  15. Size matters: Estimation sample length and electricity price forecasting accuracy By Carlo Fezzi; Luca Mosetti
  16. A switching self-exciting jump diffusion process for stock prices By Donatien Hainaut; Franck Moraux

  1. By: Achref Bachouch (UiO); C\^ome Hur\'e (LPSM UMR 8001, UPD7); Nicolas Langren\'e (CSIRO); Huyen Pham (LPSM UMR 8001, UPD7)
    Abstract: This paper presents several numerical applications of deep learning-based algorithms that have been analyzed in [11]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [6] and on quadratic Backward Stochastic Differential equations as in [5]. We also provide numerical results for an option hedging problem in finance, and energy storage problems arising in the valuation of gas storage and in microgrid management.
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1812.05916&r=cmp
  2. By: Sarfatia, M.; M., Hesamzadeha.; Holmberg, P.
    Abstract: In part I of this paper, we proposed a Mixed-Integer Linear Program (MILP) to analyse imperfect competition of oligopoly producers in two-stage zonal power markets. In part II of this paper, we propose a solution algorithm which decomposes the proposed MILP model into several subproblems and solve them in parallel and iteratively. Our solution algorithm reduces the solution time of the MILP model and it allows us to analyze largescale examples. To tackle the multiple Subgame Perfect Nash Equilibria (SPNE) situation, we propose a SPNE-band approach. The SPNE band is split into several subintervals and the proposed solution algorithm finds a representative SPNE in each subinterval. Each subinterval is independent from each other, so this structure enables us to use parallel computing. We also design a pre-feasibility test to identify the subintervals without SPNE. Our proposed solution algorithm and our SPNE-band approach are demonstrated on the 6-node and the modified IEEE 30-node example systems. The computational tractability of our solution algorithm is illustrated for the IEEE 118-node and 300-node systems.
    Keywords: Modified Benders decomposition, Multiple Subgame Perfect Nash equilibria, Parallel computing, Wholesale electricity market, Zonal pricing
    JEL: C61 C63 C72 D43 L13 L94
    Date: 2018–11–28
    URL: http://d.repec.org/n?u=RePEc:cam:camdae:1870&r=cmp
  3. By: Chang, C-L.; McAleer, M.J.; Wong, W.-K.
    Abstract: The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas.
    Keywords: Big Data, Computational science, Economics, Finance, Management, Theoretical models, Econometric and statistical models, Applications.
    Date: 2018–01–01
    URL: http://d.repec.org/n?u=RePEc:ems:eureir:112499&r=cmp
  4. By: Henry Stone
    Abstract: In this paper we use convolutional neural networks to find the H\"older exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application.
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1812.05315&r=cmp
  5. By: Daniel Philps; Tillman Weyde; Artur d'Avila Garcez; Roy Batchelor
    Abstract: Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continuous Learning Augmentation (CLA) which is based on an explicit memory structure and a feed forward neural network (FFNN) base model and used to drive long term financial investment decisions. We demonstrate that our approach improves accuracy in investment decision making while memory is addressed in an explainable way. Our approach introduces novel remember cues, consisting of empirically learned change points in the absolute error series of the FFNN. Memory recall is also novel, with contextual similarity assessed over time by sampling distances using dynamic time warping (DTW). We demonstrate the benefits of our approach by using it in an expected return forecasting task to drive investment decisions. In an investment simulation in a broad international equity universe between 2003-2017, our approach significantly outperforms FFNN base models. We also illustrate how CLA's memory addressing works in practice, using a worked example to demonstrate the explainability of our approach.
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1812.02340&r=cmp
  6. By: Ikhenaode, Bright Isaac
    Abstract: Focusing on a selected group of 19 OECD countries, we analyze the effects of immigration on natives welfare, labor market outcomes and fiscal redistribution. To this end, we build and simulate a search and matching model that allows for endogenous natives skill acquisition and intergenerational transfers. The obtained results are then compared with different variations of our benchmark model, allowing us to assess to what extent natives skill adjustment and age composition affect the impact of immigration. Our comparative statics analysis suggests that when natives adjust their skill in response to immigration, they successfully avoid, under most scenarios, any potential displacement effect in the labor market. Moreover, taking into account age composition plays a key role in assessing the fiscal impact of immigration, which turns out to be positive when we include retired workers that receive intergenerational transfers. Finally, we find that, under any scenario, our model yields more optimistic welfare effects than a standard search model that abstracts from skill decision and intergenerational redistribution. These welfare effects are found to be overall particularly positive when the migration flows comprise high-skilled workers.
    Keywords: Immigration, Welfare, Unemployment, Skill Acquisition, Fiscal Redistribution.
    JEL: F22 J24 J61 J64
    Date: 2018–11–08
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:89897&r=cmp
  7. By: Giacomo Caterini
    Abstract: Statistics on the births, deaths and survival rates of firms are crucial pieces of information, as they enter as an input in the computation of GDP, the identification of each sector’s contribution to the economy, and the assessment of gross job creation and destruction rates. Official statistics on firm demography are made available only several months after data collection and storage, however. Furthermore, unprocessed and untimely administrative data can lead to a misrepresentation of the life-cycle stage of a firm. In this paper we implement an automated version of Eurostat’s algorithm aimed at distinguishing true startup endeavors from the resurrection of pre-existing but apparently defunct firms. The potential gains from combining machine learning, natural language processing and econometric tools for pre- processing and analyzing granular data are exposed, and a machine learning method predicting reactivations of deceptively dead firms is proposed.
    Keywords: Business Demography; Classification; Text Mining
    JEL: C01 C52 C53 C80 G33 L11 L25 L26 M13 R11
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:trn:utwprg:2018/09&r=cmp
  8. By: Martin Christensen (European Commission - JRC)
    Abstract: Structural socio-economic differences across EU regions may result in heterogeneous regional responses to changes in public spending in support to R&I. In this paper we examine the socio-economic impact at the EU aggregate level and at the regional level of alternative policy designs of the future EU R&I support programme that will be put in place after 2020. For the analysis we use the RHOMOLO spatial CGE model covering 267 EU regions. Our results indicate that public spending in support to R&I can contribute to higher aggregate GDP and employment in the EU. However, the impact of public spending in support to R&I varies considerable across regions. The R&I intensive regions benefit the most in terms of GDP and employment while other regions may suffer from a shift in public spending towards R&I support programmes.
    Keywords: region, growth, Horizon Europe, Research and Innovation, impact assessment, RHOMOLO
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:ipt:termod:201805&r=cmp
  9. By: Pavel Ciaian; Andrey Ivanov; d’Artis Kancs
    Abstract: Although, the need for an efficient Roma integration policy is growing in Europe, surprisingly little robust scientific evidence regarding potential policy costs and expected benefits of alternative policy options has supported the policy design and implementation so far. The present study attempts to narrow this evidence gap and aims to shed light on long-run economic, budgetary and fiscal effects of selected education and employment policies for the inclusion of the marginalised Roma in the EU. We employ a general equilibrium approach that allows us to assess not only the direct impact of alternative Roma integration policies but also to capture all induced feedback effects. Our simulation results suggest that, although Roma integration policies would be costly for the public budget, in the medium- to long-run, economic, budgetary and fiscal benefits may significantly outweigh short- to medium-run Roma integration costs. Depending on the integration policy scenario and the analysed country, the full repayment of the integration policy investment (positive net present value) may be achieved after 7 to 9 years. In terms of the GDP, employment and earnings, the universal basic income scenario may have the highest potential, particularly in the medium- to long-run.
    Keywords: Roma, social marginalisation, education, labour market, integration policy, universal basic income.
    JEL: J6 J11 J24 O17 O43 I32
    Date: 2018–12–12
    URL: http://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2018_12&r=cmp
  10. By: Rodríguez Arosemena, Nicolás
    Abstract: This paper presents two conjectures that are the product of the reconciliation between modern economics and the long-standing jurisprudential tradition originated in Ancient Rome, whose influence is still pervasive in most of the world's legal systems. We show how these conjectures together with the theory that supports them can provide us with a powerful normative mean to solve the world's most challenging problems such as financial crises, poverty, wars, man-made environmental catastrophes and preventable deaths. The core of our theoretical framework is represented by a class of imperfect information game built completely on primitives (self-interest, human fallibility and human sociability) that we have called the Dominium Mundi Game (DMG) for reasons that will become obvious. Given the intrinsic difficulties that arise in solving this type of models, we advocate for the use of artificial intelligence as a potentially feasible method to determine the implications of the definitions and assumptions derived from the DMG's framework.
    Keywords: Game Theory; Artificial Intelligence; Dynamic Programming Squared; Imperfect Information Games; Law and Economics
    JEL: C7 C73 D6 K0
    Date: 2018–12–15
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:90560&r=cmp
  11. By: Cohen, Gregory J.; Friedrichs, Melanie; Gupta, Kamran (Federal Reserve Bank of Kansas City); Hayes, William; Lee, Seung Jung; Marsh, W. Blake (Federal Reserve Bank of Kansas City); Mislang, Nathan; Shaton, Maya; Sicilian, Martin
    Abstract: We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach. The R package for the company level match can be found on Github at https://github.com/seunglee98/fedmatch.
    Keywords: Bank Credit; Syndicated Loans; Probabilistic Matching; Company Level Matching; Loan Level Matching
    JEL: C88 E44 G21
    Date: 2018–12–03
    URL: http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp18-09&r=cmp
  12. By: Ndiaye, Abdoulaye (Federal Reserve Bank of Chicago)
    Abstract: This paper studies optimal insurance against private idiosyncratic shocks in a life-cycle model with intensive labor supply and endogenous retirement. In this environment, the optimal labor tax is hump-shaped in age: insurance benefits of taxation push for increasing-in-age taxes while rising labor supply elasticities and optimal late retirement of highly productive workers push for lowering taxes for old workers. In calibrated numerical simulations, the optimum achieves sizable welfare gains that age-dependent taxes do not deliver under the status quo US Social Security. Nevertheless, an optimal combination of age-dependent linear taxes with increasing-in-age retirement benefits generates welfare gains close to optimal.
    Keywords: Retirement; Optimal Taxation; Social Security; Continuous- Time; Optimal Stopping
    JEL: H21 H55 J26
    Date: 2017–11–03
    URL: http://d.repec.org/n?u=RePEc:fip:fedhwp:wp-2018-18&r=cmp
  13. By: José García-Montalvo; Omiros Papaspiliopoulos; Timothée Stumpf-Fétizon
    Abstract: We propose a new methodology for predicting electoral results that combines a fundamental model and national polls within an evidence synthesis framework. Although novel, the methodology builds upon basic statistical structures, largely modern analysis of variance type models, and it is carried out in open-source software. The methodology is largely motivated by the specific challenges of forecasting elections with the participation of new political parties, which is becoming increasingly common in the post-2008 European panorama. Our methodology is also particularly useful for the allocation of parliamentary seats, since the vast majority of available opinion polls predict at the national level whereas seats are allocated at local level. We illustrate the advantages of our approach relative to recent competing approaches using the 2015 Spanish Congressional Election. In general, the predictions of our model outperform the alternative specifications, including hybrid models that combine fundamental and polls' models. Our forecasts are, in relative terms, particularly accurate to predict the seats obtained by each political party.
    Keywords: multilevel model, Bayesian machine learning, inverse regression, evidence synthesis, elections
    JEL: C11 C53 C63 D72
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:bge:wpaper:1065&r=cmp
  14. By: Yusuke Narita (Cowles Foundation, Yale University); Shota Yasui (CyberAgent Inc.); Kohei Yata (Yale University)
    Abstract: What is the most statistically e?icient way to do o?-policy optimization with batch data from bandit feedback? For log data generated by contextual bandit algorithms, we consider o?line estimators for the expected reward from a counterfactual policy. Our estimators are shown to have lowest variance in a wide class of estimators, achieving variance reduction relative to standard estimators. We then apply our estimators to improve advertisement design by a major advertisement company. Consistent with the theoretical result, our estimators allow us to improve on the existing bandit algorithm with more statistical con?dence compared to a state-of-theart benchmark.
    Keywords: Machine Learning, Artificial Intelligence, Bandit Algorithm, Counterfactual Prediction, Propensity Score, Semiparametric Efficiency Bound, Advertisement Design
    Date: 2018–12
    URL: http://d.repec.org/n?u=RePEc:cwl:cwldpp:2155&r=cmp
  15. By: Carlo Fezzi; Luca Mosetti
    Abstract: Electricity price forecasting models are typically estimated via rolling windows, i.e. by using only the most recent observations. Nonetheless, the current literature does not provide much guidance on how to select the size of such windows. This paper shows that determining the appropriate window prior to estimation dramatically improves forecasting performances. In addition, it proposes a simple two-step approach to choose the best performing models and window sizes. The value of this methodology is illustrated by analyzing hourly datasets from two large power markets with a selection of ten different forecasting models. Incidentally, our empirical application reveals that simple models, such as the linear regression, can perform surprisingly well if estimated on extremely short samples.
    Keywords: electricity price forecasting, day-ahead market, parameter instability, bandwidth selection, artificial neural networks
    JEL: C22 C45 C51 C53 Q47
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:trn:utwprg:2018/10&r=cmp
  16. By: Donatien Hainaut (ESC Rennes School of Business); Franck Moraux (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This study proposes a new Markov switching process with clustering eects. In this approach, a hidden Markov chain with a nite number of states modulates the parameters of a self-excited jump process combined to a geometric Brownian motion. Each regime corresponds to a particular economic cycle determining the expected return, the diusion coecient and the long-run frequency of clustered jumps. We study rst the theoretical properties of this process and we propose a sequential Monte-Carlo method to lter the hidden state variables. We next develop a Markov Chain Monte-Carlo procedure to t the model to the S&P 500. Finally, we analyse the impact of such a jump clustering on implied volatilities of European options.
    Keywords: switching regime,Hawkes process,self-excited jumps
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:hal:journl:halshs-01909772&r=cmp

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