nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒08‒29
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Double/debiased machine learning in Stata By Achim Ahrens
  2. Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm By Koffi, Siméon
  3. Stacking generalization and machine learning in Stata By Achim Ahrens
  4. Accuracy of explanations of machine learning models for credit decisions By Andrés Alonso; José Manuel Carbó
  5. Can Machine Learning Predict Defaults in Peer-to-Peer Small Loans? By Muriuki, James M.; Badruddoza, Syed; Fuad, Syed M.
  6. Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning By Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali
  7. Prediction of WIC Program Participation: A Machine Learning Approach to Fix Reporting Error By Luo, Yufeng; Zhen, Chen
  8. Preferential Trading in Agricultural and Food Products: New Insights from a Structural Gravity Analysis and Machine Learning By Kim, Dongin; Steinbach, Sandro
  9. Distributional neural networks for electricity price forecasting By Grzegorz Marcjasz; Micha{\l} Narajewski; Rafa{\l} Weron; Florian Ziel
  10. Control and spread of contagion in networks with global effects By John Higgins; Tarun Sabarwal
  11. Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death By Fantazzini, Dean
  12. Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution By Feiyang Pan; Tongzhe Zhang; Ling Luo; Jia He; Shuoling Liu
  13. Heterogeneous effects and spillovers of macroprudential policy in an agent-based model of the UK housing market By Adrián Carro; Marc Hinterschweiger; Arzu Uluc; J. Doyne Farmer
  14. Re-examining adaptation theory using Big Data: Reactions to external shocks By Greyling, Talita; Rossouw, Stephanié
  15. Autoencoding Conditional GAN for Portfolio Allocation Diversification By Jun Lu; Shao Yi
  16. The lock-in effect of marriage: Work incentives after saying, "Yes, I do." By Christl, Michael; De Poli, Silvia; Ivaškaitė-Tamošiūnė, Viginta

  1. By: Achim Ahrens (ETH Zürich)
    Abstract: ddml implements algorithms for causal inference aided by supervised machine learning as proposed in "Double/ debiased machine learning for treatment and structural parameters" (Econometrics Journal 2018). Five different models are supported, allowing for binary or continuous treatment variables and endogeneity. ddml supports a variety of different ML programs, including lassopack and pystacked.
    Date: 2022–07–03
  2. By: Koffi, Siméon
    Abstract: This paper attempts to highlight the role of new short-term forecasting methods. It leads to the fact that artificial neural networks (LSTM) are more efficient than classical methods (ARIMA and HOLT-WINTERS) in forecasting the HICP of Côte d'Ivoire. The data are from the “Direction des Prévisions, des Politiques et des Statistiques Economiques (DPPSE)” and cover the period from January 2012 to May 2022. The root mean square error of the long-term memory recurrent neural network (LSTM) is the lowest compared to the other two techniques. Thus, one can assert that the LSTM method improves the prediction by more than 90%, ARIMA by 68%, and Holt-Winters by 61%. These results make machine learning techniques (LSTM) excellent forecasting tools.
    JEL: C15 C81 C88
    Date: 2022–08–01
  3. By: Achim Ahrens (ETH Zürich)
    Abstract: pystacked implements stacked generalization (Wolpert 1992) for regression and binary classi
    Date: 2022–07–03
  4. By: Andrés Alonso (Banco de España); José Manuel Carbó (Banco de España)
    Abstract: One of the biggest challenges for the application of machine learning (ML) models in finance is how to explain their results. In recent years, innovative interpretability techniques have appeared to assist in this task, although their usefulness is still a matter of debate within the industry. In this article we propose a novel framework to assess how accurate these techniques are. Our work is based on the generation of synthetic datasets. This allows us to define the importance of the variables, so we can calculate to what extent the explanations given by these techniques match the ground truth of our data. We perform an empirical exercise in which we apply two non-interpretable ML models (XGBoost and Deep Learning) to the synthetic datasets, , and then we explain their results using two popular interpretability techniques, SHAP and permutation Feature Importance (FI). We conclude that generating synthetic datasets shows potential as a useful approach for supervisors and practitioners who wish to assess interpretability techniques.
    Keywords: synthetic datasets, artificial intelligence, interpretability, machine learning, credit assessment
    JEL: C55 C63 G17
    Date: 2022–06
  5. By: Muriuki, James M.; Badruddoza, Syed; Fuad, Syed M.
    Keywords: Risk and Uncertainty, Institutional and Behavioral Economics, Agribusiness
    Date: 2022–08
  6. By: Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali
    Abstract: Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management.
    Date: 2022–07
  7. By: Luo, Yufeng; Zhen, Chen
    Keywords: Food Consumption/Nutrition/Food Safety, Research Methods/Statistical Methods, Health Economics and Policy
    Date: 2022–08
  8. By: Kim, Dongin; Steinbach, Sandro
    Keywords: International Relations/Trade, International Development, Research Methods/Statistical Methods
    Date: 2022–08
  9. By: Grzegorz Marcjasz; Micha{\l} Narajewski; Rafa{\l} Weron; Florian Ziel
    Abstract: We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.
    Date: 2022–07
  10. By: John Higgins (Department of Economics, University of Wisconsin, Madison, WI 53706, USA); Tarun Sabarwal (Department of Economics, University of Kansas, Lawrence, KS 66045, USA)
    Abstract: We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, computationally tractable, and efficient algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations. Our analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework.
    Keywords: Network games, coordination games, contagion, algorithmic computation
    JEL: C62 C72
    Date: 2022–04
  11. By: Fantazzini, Dean
    Abstract: This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice, alternative forecasting models, ranging from credit scoring models to machine learning and time series-based models, and different forecasting horizons. We found that the choice of the coin death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the \textit{cauchit} and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit scoring models and machine learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins' market capitalization.
    Keywords: Bitcoin, Crypto-assets, Crypto-currencies, Credit risk, Default Probability, Probability of Death, ZPP, Cauchit, Logit, Probit, Random Forests, Google Trends.
    JEL: C32 C35 C51 C53 C58 G12 G17 G32 G33
    Date: 2022
  12. By: Feiyang Pan; Tongzhe Zhang; Ling Luo; Jia He; Shuoling Liu
    Abstract: Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices? The key challenge lies in the "continuous-discrete duality" of the action space. On the one hand, the continuous action space using percentage changes in prices is preferred for generalization. On the other hand, the trader eventually needs to choose limit prices discretely due to the existence of the tick size, which requires specialization for every single stock with different characteristics (e.g., the liquidity and the price range). So we need continuous control for generalization and discrete control for specialization. To this end, we propose a hybrid RL method to combine the advantages of both of them. We first use a continuous control agent to scope an action subset, then deploy a fine-grained agent to choose a specific limit price. Extensive experiments show that our method has higher sample efficiency and better training stability than existing RL algorithms and significantly outperforms previous learning-based methods for order execution.
    Date: 2022–07
  13. By: Adrián Carro (Banco de España and University of Oxford); Marc Hinterschweiger (Bank of England); Arzu Uluc (Bank of England); J. Doyne Farmer (University of Oxford and Santa Fe Institute (New Mexico))
    Abstract: We develop an agent-based model of the UK housing market to study the impact of macroprudential policy experiments on key housing market indicators. The heterogeneous nature of this model enables us to assess the effects of such experiments on the housing, rental and mortgage markets not only in the aggregate, but also at the level of individual households and sub-segments, such as first-time buyers, homeowners, buy-to-let investors, and renters. This approach can therefore offer a broad picture of the disaggregated effects of financial stability policies. The model is calibrated using a large selection of micro-data, including data from a leading UK real estate online search engine as well as loan-level regulatory data. With a series of comparative statics exercises, we investigate the impact of: i) a hard loan-to-value limit, and ii) a soft loan-to-income limit, allowing for a limited share of unconstrained new mortgages. We find that, first, these experiments tend to mitigate the house price cycle by reducing credit availability and therefore leverage. Second, an experiment targeting a specific risk measure may also affect other risk metrics, thus necessitating a careful calibration of the policy to achieve a given reduction in risk. Third, experiments targeting the owner-occupier housing market can spill over to the rental sector, as a compositional shift in home ownership from owner-occupiers to buy-to-let investors affects both the supply of and demand for rental properties.
    Keywords: agent-based modelling, housing market, rental market, macroprudential policy, borrower-based measures
    JEL: D1 D31 E58 G51 R21 R31
    Date: 2022–05
  14. By: Greyling, Talita; Rossouw, Stephanié
    Abstract: During the global response to COVID-19, the analogy of fighting a war was often used. In 2022, the world faced a different war altogether, an unprovoked Russian invasion of Ukraine. Since 2020 the world has faced these unprecedented shocks. Although we realise these events' health and economic effects, more can be known about the happiness effects on the people in a country and how it differs between a health and a war shock. Additionally, we need to investigate if these external shocks do affect wellbeing, how they differ from one another, and how long it takes happiness to adapt to these shocks. Therefore, this paper aims to compare these two external shocks for ten countries spanning the Northern and Southern hemispheres to investigate the effect on happiness. By investigating the aforementioned, we also re-examine the adaptation theory and see whether it holds at the country level. We use a unique dataset derived from tweets extracted in real-time per country. We derive each tweet's underlying sentiment by applying Natural Language Processing (machine learning). Using the sentiment score, we apply algorithms to construct daily time-series data to measure happiness (Gross National Happiness (GNH)). Our Twitter dataset is combined with data from Oxford's COVID-19 Government Response Tracker. We find that in both instances, the external shocks caused a decrease in GNH. Considering both types of shocks, the adaptation to previous happiness levels occurred within weeks. Understanding the effects of external shocks on happiness is essential for policymakers as effects on happiness have a spillover effect on other variables such as production, safety and trust. Furthermore, the additional macro-level results on the adaptation theory contribute to previously unexplored fields of study.
    Date: 2022
  15. By: Jun Lu; Shao Yi
    Abstract: Over the decades, the Markowitz framework has been used extensively in portfolio analysis though it puts too much emphasis on the analysis of the market uncertainty rather than on the trend prediction. While generative adversarial network (GAN) and conditional GAN (CGAN) have been explored to generate financial time series and extract features that can help portfolio analysis. The limitation of the CGAN framework stands in putting too much emphasis on generating series rather than keeping features that can help this generator. In this paper, we introduce an autoencoding CGAN (ACGAN) based on deep generative models that learns the internal trend of historical data while modeling market uncertainty and future trends. We evaluate the model on several real-world datasets from both the US and Europe markets, and show that the proposed ACGAN model leads to better portfolio allocation and generates series that are closer to true data compared to the existing Markowitz and CGAN approaches.
    Date: 2022–06
  16. By: Christl, Michael; De Poli, Silvia; Ivaškaitė-Tamošiūnė, Viginta
    Abstract: In this paper, we use EUROMOD, the tax-benefit microsimulation model of the European Union, to investigate the impact of marriage-related tax-benefit instruments on the labour supply of married couples. For each married partner, we estimate their individual marginal effective tax rate and net replacement rate before and after marriage. We show that the marriage bonus, which is economically significant in eight European countries, decreases the work incentives for women and, particularly, on the intensive margin. In contrast, the incentives on the intensive margin increase for men once they are married, pointing to the marriage-biased and gender-biased taxbenefit structures in the analysed countries. Our results suggest that marriage bonuses contribute to a lock-in effect, where second earners, typically women, are incentivised to work less, with negative economic consequences.
    Keywords: marriage,cohabitation,marriage bonus,work incentives,gender,tax-benefit system,labour supply,Europe
    JEL: H31 J12 J22
    Date: 2022

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