nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒05‒16
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. On Parametric Optimal Execution and Machine Learning Surrogates By Tao Chen; Mike Ludkovski; Moritz Vo{\ss}
  2. New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning By Sabyasachi Kar; Amaani Bashir; Mayank Jain
  3. Cryptocurrency Return Prediction Using Investor Sentiment Extracted by BERT-Based Classifiers from News Articles, Reddit Posts and Tweets By Duygu Ider
  4. Variational Heteroscedastic Volatility Model By Zexuan Yin; Paolo Barucca
  5. Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression By Vito Polito; Yunyi Zhang
  6. Fiscal and Monetary Policies in an Agent-Based Model By Pongpitch Amatyakul; Nutnicha Theppornpitak
  7. A Dual Generalized Long Memory Modelling for Forecasting Electricity Spot Price: Neural Network and Wavelet Estimate By Souhir Ben Amor; Heni Boubaker; Lotfi Belkacem
  8. Lyapunov based Stochastic Stability of Human-Machine Interaction: A Quantum Decision System Approach By Luke Snow; Shashwat Jain; Vikram Krishnamurthy
  9. Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment By Augustine Denteh; Helge Liebert
  10. Programming FPGAs for Economics: An Introduction to Electrical Engineering Economics By Bhagath Cheela; André DeHon; Jesús Fernández-Villaverde; Alessandro Peri
  11. European enterprise survey on the use of technologies based on artificial intelligence By Snezha SK Kazakova; Allison AD Dunne; Daan DB Bijwaard; Julien Gosse; Charles Hoffreumon; Nicolas van Zeebroeck
  12. Data Production and the coevolving AI trajectories: An attempted evolutionary model. By Andrea Borsato; Andre Lorentz
  13. What Drives Mortgage Default Risk in Europe and the U.S.? By Mr. Thierry Tressel; Eugen Tereanu; Mr. Marco Gross; Xiaodan Ding

  1. By: Tao Chen; Mike Ludkovski; Moritz Vo{\ss}
    Abstract: We investigate optimal execution problems with instantaneous price impact and stochastic resilience. First, in the setting of linear price impact function we derive a closed-form recursion for the optimal strategy, generalizing previous results with deterministic transient price impact. Second, we develop a numerical algorithm for the case of nonlinear price impact. We utilize an actor-critic framework that constructs two neural-network surrogates for the value function and the feedback control. One advantage of such functional approximators is the ability to do parametric learning, i.e. to incorporate some of the model parameters as part of the input space. Precise calibration of price impact, resilience, etc., is known to be extremely challenging and hence it is critical to understand sensitivity of the strategy to these parameters. Our parametric neural network (NN) learner organically scales across 3-6 input dimensions and is shown to accurately approximate optimal strategy across a range of parameter configurations. We provide a fully reproducible Jupyter Notebook with our NN implementation, which is of independent pedagogical interest, demonstrating the ease of use of NN surrogates in (parametric) stochastic control problems.
    Date: 2022–04
  2. By: Sabyasachi Kar; Amaani Bashir; Mayank Jain (Institute of Economic Growth, Delhi)
    Abstract: The use of big data and machine learning techniques is now very common in many spheres and there is growing popularity of these approaches in macroeconomic forecasting as well. Is big data and machine learning really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? What are the tradeoffs that forecasters need to keep in mind, and what are the steps they need to take to use these resources effectively? We carry out a critical analysis of the existing literature in order to answer these questions. Our analysis suggests that the answer to most of these questions are nuanced, conditional on a number of factors identified in the study.
    Keywords: Forecasting, Big Data, Machine Learning, Supervised Learning, Meta-analysis, Growth, Inflation
    JEL: C14 C45 C52 C53 C55 E17 E37
    Date: 2021–10
  3. By: Duygu Ider
    Abstract: This paper studies the extent at which investor sentiment contributes to cryptocurrency return prediction. Investor sentiment is extracted from news articles, Reddit posts and Tweets using BERT-based classifiers fine-tuned on this specific text data. As this data is unlabeled, a weak supervision approach by pseudo-labeling using a zero-shot classifier is used. Contribution of sentiment is then examined using a variety of machine learning models. Each model is trained on data with and without sentiment separately. The conclusion is that sentiment leads to higher prediction accuracy and additional investment profit when the models are analyzed collectively, although this does not hold true for every single model.
    Date: 2022–04
  4. By: Zexuan Yin; Paolo Barucca
    Abstract: We propose Variational Heteroscedastic Volatility Model (VHVM) -- an end-to-end neural network architecture capable of modelling heteroscedastic behaviour in multivariate financial time series. VHVM leverages recent advances in several areas of deep learning, namely sequential modelling and representation learning, to model complex temporal dynamics between different asset returns. At its core, VHVM consists of a variational autoencoder to capture relationships between assets, and a recurrent neural network to model the time-evolution of these dependencies. The outputs of VHVM are time-varying conditional volatilities in the form of covariance matrices. We demonstrate the effectiveness of VHVM against existing methods such as Generalised AutoRegressive Conditional Heteroscedasticity (GARCH) and Stochastic Volatility (SV) models on a wide range of multivariate foreign currency (FX) datasets.
    Date: 2022–04
  5. By: Vito Polito (Department of Economics, University of Sheffield, UK); Yunyi Zhang (Xiamen University, China)
    Abstract: We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure non-explosive dynamics. As such, it is employable to account for nonlinearity in macroeconomic dynamics not only during typical business cycles but also in a wide range of extreme events, like deep recessions and strong expansions. The methodology is put to the test using aggregate data for the United States that include the abnormal realizations during the recent Covid-19 pandemic. The model delivers plausible and stable structural inference, and accurate out-of-sample forecasts. This performance compares favourably against a number of alternative methodologies recently proposed to deal with large outliers in macroeconomic data caused by the pandemic.
    Keywords: Tax avoidance; Nonlinear time series; Regime switching models; Extreme events; Covid-19; Macroeconomic forecasting
    JEL: C45 C5 E37
    Date: 2022–03
  6. By: Pongpitch Amatyakul; Nutnicha Theppornpitak
    Abstract: In this paper, we aim to assess the impacts of using monetary policies and fiscal transfers on the economy using an agent-based model. The model used is based on the original model developed by Ashraf et al. (2017), where agents endogenously develop trading networks of goods and labor, to study the impacts of the banking sector, and extended by Popoyan et al. (2017) to include different policy rate rules and macroprudential policy. We evaluate different fiscal policies and their interactions with monetary policy on how the economy performs based on aggregates such as total output and inflation, as well as based on granular data such as the wealth and consumption of the agents at specific percentiles. The findings are that consumption-based policies are best for reducing the aggregate effects on GDP, targeted policies are efficient if the government's goal is to help a specific group, and unconditional transfers are the least efficient of the three. In addition, we analyze the effects of implementing monetary and fiscal policies synchronously after a COVID-19-like crisis, and we do not find conclusive evidence that combining the two policies are better than the sum of the individual effects, but it is likely to be necessary to do both in order to get the economy back to its original path in a timely manner.
    Keywords: Monetary policy; Fiscal policy; Simulation
    JEL: E52 E62
    Date: 2022–04
  7. By: Souhir Ben Amor; Heni Boubaker; Lotfi Belkacem
    Abstract: In this paper, dual generalized long memory modelling has been proposed to predict the electricity spot price. First, we focus on modelling the conditional mean of the series so we adopt a generalized fractional k-factor Gegenbauer process ( k-factor GARMA). Secondly, the residual from the k-factor GARMA model has been used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using two different learning algorithms, so we estimate the hybrid k- factor GARMA-LLWNN based backpropagation (BP) algorithm and based particle swarm optimization (PSO) algorithm. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has been adopted, and the parameters of the k-factor GARMAG- GARCH model have been estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. To illustrate the usefulness of our methodology, we carry out an empirical application using the hourly returns of electricity prices from the Nord Pool market. The empirical results have shown that the k-factor GARMA-G-GARCH model has the best prediction accuracy in terms of forecasting criteria, and find that this is more appropriate for forecasts.
    Date: 2022–04
  8. By: Luke Snow; Shashwat Jain; Vikram Krishnamurthy
    Abstract: In mathematical psychology, decision makers are modeled using the Lindbladian equations from quantum mechanics to capture important human-centric features such as order effects and violation of the sure thing principle. We consider human-machine interaction involving a quantum decision maker (human) and a controller (machine). Given a sequence of human decisions over time, how can the controller dynamically provide input messages to adapt these decisions so as to converge to a specific decision? We show via novel stochastic Lyapunov arguments how the Lindbladian dynamics of the quantum decision maker can be controlled to converge to a specific decision asymptotically. Our methodology yields a useful mathematical framework for human-sensor decision making. The stochastic Lyapunov results are also of independent interest as they generalize recent results in the literature.
    Date: 2022–03
  9. By: Augustine Denteh; Helge Liebert
    Abstract: We provide new insights regarding the finding that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The treatment effect distribution is widely dispersed, and the average effect is not representative of most individualized treatment effects. A small group—about 14% of participants—in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and prior utilization. Intensive margin effects are an important driver of increases in ED utilization.
    Keywords: Medicaid, ED use, effect heterogeneity, causal machine learning, optimal policy
    JEL: H75 I13 I38
    Date: 2022
  10. By: Bhagath Cheela; André DeHon; Jesús Fernández-Villaverde; Alessandro Peri
    Abstract: We show how to use field-programmable gate arrays (FPGAs) and their associated high-level synthesis (HLS) compilers to solve heterogeneous agent models with incomplete markets and aggregate uncertainty (Krusell and Smith, 1998). We document that the acceleration delivered by one single FPGA is comparable to that provided by using 74 CPU cores in a conventional cluster. We describe how to achieve multiple acceleration opportunities—pipeline, data-level parallelism, and data precision—with minimal modification of the C code written for a traditional sequential processor, which we then deploy on FPGAs easily available at Amazon Web Services. We quantify the speedup and cost of these accelerations. Our paper is the first step toward a new field, electrical engineering economics, focused on designing computational accelerators for economics to tackle challenging quantitative models.
    JEL: C60 C63 C88 D52
    Date: 2022–04
  11. By: Snezha SK Kazakova; Allison AD Dunne; Daan DB Bijwaard; Julien Gosse; Charles Hoffreumon; Nicolas van Zeebroeck
    Date: 2020–07–28
  12. By: Andrea Borsato; Andre Lorentz
    Abstract: This paper contributes to the understanding of the relationship between the nature of data and the Artificial Intelligence (AI) technological trajectories. We develop an agentbased model in which firms are data producers that compete on the markets for data and AI. The model is enriched by a public sector that fuels the purchase of data and trains the scientists that will populate firms as workforce. Through several simulation experiments we analyze the determinants of each market structure, the corresponding relationships with innovation attainments, the pattern followed by labour and data productivity, and the quality of data traded in the economy. More precisely, we question the established view in the literature on industrial organization according to which technological imperatives are enough to experience divergent industrial dynamics on both the markets for data and AI blueprints. Although technical change behooves if any industry pattern is to emerge, the actual unfolding is not the outcome of a specific technological trajectory, but the result of the interplay between technology-related factors and the availability of data-complementary inputs such as labour and AI capital, the market size, preferences and public policies.
    Keywords: Artificial Intelligence, Data Markets, Industrial Dynamics, Agent-based Models.
    JEL: L10 L60 O33 O38
    Date: 2022
  13. By: Mr. Thierry Tressel; Eugen Tereanu; Mr. Marco Gross; Xiaodan Ding
    Abstract: We present an analysis of the sensitivity of household mortgage probabilities of default (PDs) and loss given default (LGDs) on unemployment rates, house price growth, interest rates, and other drivers. A structural micro-macro simulation model is used to that end. It is anchored in the balance sheets and income-expense flow data from about 95,000 households and 230,000 household members from 21 EU countries and the U.S. We present country-specific nonlinear regressions based on the structural model simulation-implied relation between PDs and LGDs and their drivers. These can be used for macro scenario-conditional forecasting, without requiring the conduct of the micro simulation. We also present a policy counterfactual analysis of the responsiveness of mortgage PDs, LGDs, and bank capitalization conditional on adverse scenarios related to the COVID-19 pandemic across all countries. The economics of debt moratoria and guarantees are discussed against the background of the model-based analysis.
    Keywords: Credit risk, household sector, micro-macro simulation modeling, financial policies
    Date: 2022–04–01

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