nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒01‒04
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. A General and Efficient Method for Solving Regime-Switching DSGE Models By Julien Albertini; Stéphane Moyen
  2. Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data By Aaron Wray; Matthew Meades; Dave Cliff
  3. Investigating the effects of environmental and energy policies in Turkey using an energy-disaggregated CGE model By Dizem Ertac
  4. Trajectory Based Distributionally Robust Optimization Applied to the Case of Electricity Facilities Investment with Significant Penetration of Renewables By Pierre Cayet; Arash Farnoosh
  5. Die Bedeutung individuellen Verhaltens über den Jahreswechsel für die Weiterentwicklung der Covid-19-Pandemie in Deutschland By Gabler, Janos; Raabe, Tobias; Röhrl, Klara; Gaudecker, Hans-Martin von
  6. Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds By Elena Ivona DUMITRESCU; Sullivan HUE; Christophe HURLIN; Sessi TOKPAVI
  7. Developing Artificial Intelligence Sustainably By Gordon Myers; Kiril Nejkov
  8. Portfolio Optimisation Using the D-Wave Quantum Annealer By Frank Phillipson; Harshil Singh Bhatia
  9. Artificial Intelligence in the Power Sector By Baloko Makala; Tonci Bakovic
  10. Impact of Weather Factors on Migration Intention using Machine Learning Algorithms By Juhee Bae; John Aoga; Stefanija Veljanoska; Siegfried Nijssen; Pierre Schaus
  11. Double machine learning for sample selection models By Michela Bia; Martin Huber; Luk\'a\v{s} Laff\'ers
  12. Start Spreading the News: News Sentiment and Economic Activity in Australia By Kim Nguyen; Gianni La Cava

  1. By: Julien Albertini (Univ Lyon, Université Lumière Lyon 2, GATE UMR 5824, F-69130 Ecully, France); Stéphane Moyen (Deutsche Bundesbank)
    Abstract: This paper provides a general representation of endogenous and threshold-based regime switching models and develops an efficient numerical solution method. The regime-switching is triggered endogenously when some variables cross threshold conditions that can themselves be regime-dependent. We illustrate our approach using a RBC model with state-dependent government spending policies. It is shown that regime-switching models involve strong non linearities and discontinuities in the dynamics of the model. However, our numerical solution based on simulation and projection methods with regime-dependent policy rules is accurate, and fast enough, to efficiently take into all these challenging aspects. Several alternative specifications to the model and the method are studied.
    Keywords: Regime-switching, RBC model, simulation, accuracy
    JEL: E3 J6
    Date: 2020
  2. By: Aaron Wray; Matthew Meades; Dave Cliff
    Abstract: We present results demonstrating that an appropriately configured deep learning neural network (DLNN) can automatically learn to be a high-performing algorithmic trading system, operating purely from training-data inputs generated by passive observation of an existing successful trader T. That is, we can point our black-box DLNN system at trader T and successfully have it learn from T's trading activity, such that it trades at least as well as T. Our system, called DeepTrader, takes inputs derived from Level-2 market data, i.e. the market's Limit Order Book (LOB) or Ladder for a tradeable asset. Unusually, DeepTrader makes no explicit prediction of future prices. Instead, we train it purely on input-output pairs where in each pair the input is a snapshot S of Level-2 LOB data taken at the time when T issued a quote Q (i.e. a bid or an ask order) to the market; and DeepTrader's desired output is to produce Q when it is shown S. That is, we train our DLNN by showing it the LOB data S that T saw at the time when T issued quote Q, and in doing so our system comes to behave like T, acting as an algorithmic trader issuing specific quotes in response to specific LOB conditions. We train DeepTrader on large numbers of these S/Q snapshot/quote pairs, and then test it in a variety of market scenarios, evaluating it against other algorithmic trading systems in the public-domain literature, including two that have repeatedly been shown to outperform human traders. Our results demonstrate that DeepTrader learns to match or outperform such existing algorithmic trading systems. We analyse the successful DeepTrader network to identify what features it is relying on, and which features can be ignored. We propose that our methods can in principle create an explainable copy of an arbitrary trader T via "black-box" deep learning methods.
    Date: 2020–11
  3. By: Dizem Ertac
    Abstract: This thesis investigates environmental and energy policies that Turkey needs to adopt on its way to a sustainable development path. A comparative-static, multi-sectoral CGE model, TurkMod, is developed in order to analyze the potential scenarios available for the Turkish economy to attain a low-carbon society with a reduced reliance on fossil fuel imports. Domestic energy demand has significantly increased in Turkey over the past decades and this has put a lot of pressure on policy-makers as the economy greatly depends on imports of natural gas and oil as far as current energy consumption is concerned. The CGE model in this study is based on a 2012 energy-disaggregated Social Accounting Matrix (SAM) constructed as a part of this thesis as well. The energy-disaggregated SAM incorporates 18 sectors for production activities, 11 products as commodities, 2 factors of production as labor and capital, 3 institutional accounts as firms, households, and the government, a separate account for taxes on commodities, taxes on production and taxes on different types of factor use, a capital account, and finally the rest of the world (ROW) account. Disaggregating the electricity sector to include 8 different types of power generating sectors (5 of which are renewable energy sources) enables electric power substitution in the model. The energy-disaggregated SAM is further linked with satellite accounts which include data on derived energy demand and greenhouse gas (GHG) emissions.The macroeconomic and environmental impacts of four distinct sets of scenarios are analyzed with respect to the baseline scenario. The first scenario simulates a 30% increase in energy efficiency in the production sectors and the residential sector and evidence is found for reaching the 21% GHG mitigation target set in Turkey’s pledge for Paris Agreement compliance. The second set of scenarios is the inclusion of a medium-level and high-level carbon tax rates for coal, oil and natural gas. The carbon tax scenarios produce significant effects on both emission reduction targets and substituting fossil fuel technologies with cleaner energy types. The third scenario investigates the sectoral and welfare impacts of providing subsidies for renewable energy sources. Turkey has already adopted a scheme where renewable energies are beings subsidized and promoted, however, this policy does not produce the necessary transformation for the Turkish society when utilized solely on its own. The fourth scenario estimates the effects of changes in world prices of energy on the Turkish economy. A 20% increase in world energy prices, i.e. oil, natural gas, and coal, induces substantial changes in the breakdown of TPES and the power-generating sector, but this scenario is a rather hypothetical one as it cannot be suggested as a viable policy option. All in all, these potential energy scenarios have significant and influential impacts on the Turkish economy and its environment. Notwithstanding, a carbon tax policy proves to be the most viable scenario which leads to reduced energy intensities in all sectors, a 21% GHG emissions abatement, and a transformation of the energy sector towards having a low-carbon content along with a reduced reliance on fossil fuel imports.
    Keywords: general equilibrium modeling; energy and environmental policies
    Date: 2020–12–14
  4. By: Pierre Cayet; Arash Farnoosh
    Abstract: As large scale penetration of renewables into electric systems requires increasing flexibility from dispatchable production units, the electricity mix must be adapted to brutal variations of residual demand. Using tools from distributionally robust optimization (DRO), we propose a trajectory ambiguity set including residual demand trajectories answering both support and variability criterion using quantile information, and approximate the level-maximizing and variability-maximizing residual demand trajectories using two simple algorithms. These two limiting trajectories allow us to make investment decisions robust to extremely high levels and brutal variations of residual demand. We provide a numerical experiment using a MILP investment and unit commitment model in the case of France and discuss the results.
    Keywords: OR in energy; Uncertainty modelling; Decision analysis; Renewables
    JEL: C61
    Date: 2020
  5. By: Gabler, Janos (IZA); Raabe, Tobias (quantilope); Röhrl, Klara (University of Bonn); Gaudecker, Hans-Martin von (University of Bonn)
    Abstract: Wir nutzen ein neues Modell, um den Verlauf der Covid-19-Pandemie über die Weihnachtstage und den Jahreswechsel vorherzusagen. Während die weitgehende Schließung der Betriebe neben den verlängerten Schulferien die Infektionszahlen drücken, werden Reiseaktivitäten und Weihnachtsfeiern zu einem starken Anstieg führen. Unsere Ergebnisse geben wenig Anlass zur Hoffnung, dass die Infektionszahlen über die Weihnachtstage und den Jahreswechsel nennenswert zurückgehen. Eher dürfte das Gegenteil der Fall sein. Einen großen Effekt kann die private Kontaktnachverfolgung ausmachen. Wenn alle Teilnehmer von Weihnachtsfeierlichkeiten über einen später auftretenden Infektionsverdacht (Symptome oder positiver Test) umgehend benachrichtigt werden und ihre Kontakte reduzieren, könnten mehrere hunderttausend Infektionen in der ersten Januarhälfte vermieden werden.
    Keywords: COVID-19, agent based simulation model, public health measures
    JEL: C63 I18
    Date: 2020–12
  6. By: Elena Ivona DUMITRESCU; Sullivan HUE; Christophe HURLIN; Sessi TOKPAVI
    Keywords: , Risk management, Credit scoring, Credit scoring, Machine learning, Interpretability
    Date: 2020
  7. By: Gordon Myers; Kiril Nejkov
    Keywords: Information and Communication Technologies - ICT Policy and Strategies Private Sector Development - Business Ethics, Leadership and Values Private Sector Development - Emerging Markets Science and Technology Development - Technology Innovation
    Date: 2020–03
  8. By: Frank Phillipson; Harshil Singh Bhatia
    Abstract: The first quantum computers are expected to perform well at quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S\&P500 index, using the state-of-the-art implementation of D-Wave's quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state-of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.
    Date: 2020–11
  9. By: Baloko Makala; Tonci Bakovic
    Keywords: Energy - Electric Power Energy - Energy Demand Energy - Renewable Energy Information and Communication Technologies - ICT Applications Information and Communication Technologies - Information Technology
    Date: 2020–04
  10. By: Juhee Bae (University of Skovde, Sweden); John Aoga (University of Abomey-Calavi, Bénin); Stefanija Veljanoska (Université de Rennes 1, France); Siegfried Nijssen (ICTEAM, Université catholique de Louvain); Pierre Schaus (ICTEAM, Université catholique de Louvain)
    Abstract: A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approach. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual’s intention to migrate in the six agriculture-dependent economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.
    Keywords: Migration, Weather shocks, Machine learning, Tree-based algorithms
    Date: 2020–11–02
  11. By: Michela Bia; Martin Huber; Luk\'a\v{s} Laff\'ers
    Abstract: This paper considers treatment evaluation when outcomes are only observed for a subpopulation due to sample selection or outcome attrition/non-response. For identification, we combine a selection-on-observables assumption for treatment assignment with either selection-on-observables or instrumental variable assumptions concerning the outcome attrition/sample selection process. To control in a data-driven way for potentially high dimensional pre-treatment covariates that motivate the selection-on-observables assumptions, we adapt the double machine learning framework to sample selection problems. That is, we make use of (a) Neyman-orthogonal and doubly robust score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias. We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under specific regularity conditions concerning the machine learners. The estimator is available in the causalweight package for the statistical software R.
    Date: 2020–11
  12. By: Kim Nguyen (Reserve Bank of Australia); Gianni La Cava (Reserve Bank of Australia)
    Abstract: In times of crisis, real-time indicators of economic activity are a critical input to timely and well-targeted policy responses. The COVID-19 pandemic is the most recent example of a crisis where events with little historical precedent played out rapidly and unpredictably. To address this need for real-time indicators we develop a new indicator of 'news sentiment' based on a combination of text analysis, machine learning and newspaper articles. The news sentiment index complements other timely economic indicators and has the advantage of potentially being updated on a daily basis. It captures key macroeconomic events, such as economic downturns, and typically moves ahead of survey-based measures of sentiment. Changes in sentiment expressed in monetary policy-related news can also partly explain unexpected changes in monetary policy. This suggests that news captures important, but unobserved, information about the risks to the RBA's forecasts that the RBA responds to when setting interest rates. An event study in the days around monetary policy decisions suggests that an unexpected tightening in monetary policy is associated with weaker news sentiment, though the effects on sentiment are temporary and not particularly strong.
    Keywords: news media; sentiment; economic activity; text analysis; machine learning
    JEL: E32 E52
    Date: 2020–12

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