nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒10‒14
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Will Artificial Intelligence Replace Computational Economists Any Time Soon? By Maliar, Lilia; Maliar, Serguei; Winant, Pablo
  2. When the U.S. catches a cold, Canada sneezes: a lower-bound tale told by deep learning By Lepetyuk, Vadym; Maliar, Lilia; Maliar, Serguei
  3. Macroeconomic Indicator Forecasting with Deep Neural Networks By Thomas Cook
  4. Benchmarking Global Optimizers By Antoine Arnoud; Fatih Guvenen; Tatjana Kleineberg
  5. Solving Heterogeneous Agent Models with Non-convex Optimization Problems: Linearization and Beyond % By Michael Reiter
  6. Application of Machine Learning in Forecasting International Trade Trends By Feras Batarseh; Munisamy Gopinath; Ganesh Nalluru; Jayson Beckman
  7. The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods By Lisha Lin; Yaqiong Li; Rui Gao; Jianhong Wu
  8. Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation By Alexis Bogroff; Dominique Guégan
  9. Assessment of the tax and transfer changes in Hungary between 2010 and 2017 using a microsimulation model By Mihály Szoboszlai; Zoltán Bögöthy; Pálma Mosberger; Dávid Berta
  10. Imposing Equilibrium Restrictions in the Estimation of Dynamic Discrete Games By Aguirregabiria, Victor; Marcoux, Mathieu
  11. Distributionally Robust XVA via Wasserstein Distance Part 1: Wrong Way Counterparty Credit Risk By Derek Singh; Shuzhong Zhang
  12. Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk By Derek Singh; Shuzhong Zhang
  13. Nonzero-sum stochastic differential games with impulse controls: a verification theorem with applications By Aïd, René; Basei, Matteo; Callegaro, Giorgia; Campi, Luciano; Vargiolu, Tiziano

  1. By: Maliar, Lilia; Maliar, Serguei; Winant, Pablo
    Abstract: Artificial intelligence (AI) has impressive applications in many fields (speech recognition, computer vision, etc.). This paper demonstrates that AI can be also used to analyze complex and high-dimensional dynamic economic models. We show how to convert three fundamental objects of economic dynamics -- lifetime reward, Bellman equation and Euler equation -- into objective functions suitable for deep learning (DL). We introduce all-in-one integration technique that makes the stochastic gradient unbiased for the constructed objective functions. We show how to use neural networks to deal with multicollinearity and perform model reduction in Krusell and Smith's (1998) model in which decision functions depend on thousands of state variables -- we literally feed distributions into neural networks! In our examples, the DL method was reliable, accurate and linearly scalable. Our ubiquitous Python code, built with Dolo and Google TensorFlow platforms, is designed to accommodate a variety of models and applications.
    Keywords: artificial intelligence; Bellman equation; deep learning; Dynamic Models; Dynamic programming; Euler Equation; Machine Learning; neural network; stochastic gradient; value function
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:14024&r=all
  2. By: Lepetyuk, Vadym; Maliar, Lilia; Maliar, Serguei
    Abstract: The Canadian economy was not initially hit by the 2007-2009 Great Recession but ended up having a prolonged episode of the effective lower bound (ELB) on nominal interest rates. To investigate the Canadian ELB experience, we build a "baby" ToTEM model -- a scaled-down version of the Terms of Trade Economic Model (ToTEM) of the Bank of Canada. Our model includes 49 nonlinear equations and 21 state variables. To solve such a high-dimensional model, we develop a projection deep learning algorithm -- a combination of unsupervised and supervised (deep) machine learning techniques. Our findings are as follows: The Canadian ELB episode was contaminated from abroad via large foreign demand shocks. Prolonged ELB episodes are easy to generate in open-economy models, unlike in closed-economy models. Nonlinearities associated with the ELB constraint have virtually no impact on the Canadian economy but other nonlinearities do, in particular, the degree of uncertainty and specific closing condition used to induce the model's stationarity.
    Keywords: central banking; clustering analysis large-scale model; deep learning; Machine Learning; neural networks; New Keynesian Model; supervised learning; ToTEM; ZLB
    JEL: C61 C63 C68 E31 E52
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:14025&r=all
  3. By: Thomas Cook (Federal Reserve Bank of Kansas City)
    Abstract: Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:red:sed019:402&r=all
  4. By: Antoine Arnoud; Fatih Guvenen; Tatjana Kleineberg
    Abstract: We benchmark seven global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are taken from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The other two algorithms are versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Non-linear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use a set of benchmarking tools recently developed in the applied mathematics literature. We find that the success rate of many optimizers vary dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer on both the math test functions and the economic application. The next-best performing optimizers are StoGo and CRS for the test functions and MLSL for the economic application.
    JEL: C13 C15 C51 C53 C61 C63 D52 J31
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26340&r=all
  5. By: Michael Reiter (Institute for Advanced Studies)
    Abstract: This paper presents methods for heterogeneous agent models where agents solve non-convex optimization problems. It shows how to apply the linearization approach of Reiter (2009) to non-convex models, and develops a theory of state and value function reduction to handle models with very large state spaces. It shows the potential problems of the linearization approach and ways to diagnose them. To overcome these problems, global nonlinear solution algorithms are presented, based on temporary equilibrium concepts. The methods are applied to models with heterogeneous households and indivisible labor, as well as to a model of heterogeneous firms with lumpy investment. \end{abstract}
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:red:sed019:1048&r=all
  6. By: Feras Batarseh; Munisamy Gopinath; Ganesh Nalluru; Jayson Beckman
    Abstract: International trade policies have recently garnered attention for limiting cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and beef). Since trade critically affects employment and wages, predicting future patterns of trade is a high-priority for policy makers around the world. While traditional economic models aim to be reliable predictors, we consider the possibility that Machine Learning (ML) techniques allow for better predictions to inform policy decisions. Open-government data provide the fuel to power the algorithms that can explain and forecast trade flows to inform policies. Data collected in this article describe international trade transactions and commonly associated economic factors. Machine learning (ML) models deployed include: ARIMA, GBoosting, XGBoosting, and LightGBM for predicting future trade patterns, and K-Means clustering of countries according to economic factors. Unlike short-term and subjective (straight-line) projections and medium-term (aggre-gated) projections, ML methods provide a range of data-driven and interpretable projections for individual commodities. Models, their results, and policies are introduced and evaluated for prediction quality.
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1910.03112&r=all
  7. By: Lisha Lin; Yaqiong Li; Rui Gao; Jianhong Wu
    Abstract: In the paper, the pricing of Quanto options is studied, where the underlying foreign asset and the exchange rate are correlated with each other. Firstly, we adopt Bayesian methods to estimate unknown parameters entering the pricing formula of Quanto options, including the volatility of stock, the volatility of exchange rate and the correlation. Secondly, we compute and predict prices of different four types of Quanto options based on Bayesian posterior prediction techniques and Monte Carlo methods. Finally, we provide numerical simulations to demonstrate the advantage of Bayesian method used in this paper comparing with some other existing methods. This paper is a new application of the Bayesian methods in the pricing of multi-asset options.
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1910.04075&r=all
  8. By: Alexis Bogroff (Université Paris1 Panthéon-Sorbonne); Dominique Guégan (Université Paris1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, LabEx ReFi and Ca' Foscari University of Venezia)
    Abstract: An extensive list of risks relative to big data frameworks and their use through models of artificial intelligence is provided along with measurements and implementable solutions. Bias, interpretability and ethics are studied in depth, with several interpretations from the point of view of developers, companies and regulators. Reflexions suggest that fragmented frameworks increase the risks of models misspecification, opacity and bias in the result; Domain experts and statisticians need to be involved in the whole process as the business objective must drive each decision from the data extraction step to the final activatable prediction. We propose an holistic and original approach to take into account the risks encountered all along the implementation of systems using artificial intelligence from the choice of the data and the selection of the algorithm, to the decision making
    Keywords: Artificial Intelligence; Bias; Big Data; Ethics; Governance; Interpretability; Regulation; Risk
    JEL: C4 C5 C6 C8 D8 G28 G38 K2
    Date: 2019–06
    URL: http://d.repec.org/n?u=RePEc:mse:cesdoc:19012&r=all
  9. By: Mihály Szoboszlai (Magyar Nemzeti Bank (Central Bank of Hungary)); Zoltán Bögöthy (Magyar Nemzeti Bank (Central Bank of Hungary)); Pálma Mosberger (Magyar Nemzeti Bank (Central Bank of Hungary)); Dávid Berta (Magyar Nemzeti Bank (Central Bank of Hungary))
    Abstract: In this study, we analyse the immediate budgetary and the long-term macroeconomic and fiscal effects of measures concerning taxes, contributions and transfers in the period between 2010 and 2017 with a microsimulation model. The corresponding tool is an updated and extended version of the behavioural general equilibrium microsimulation model developed by the Magyar Nemzeti Bank. Among the relevant fiscal policy measures, we primarily took into account the changes in labour taxes, the main elements of the transformation of social benefits and indirectly several other tax changes (on sales, capital and consumption taxes) over the period between 2010 and 2017. Our results suggest that the policy measures examined might contribute to Hungarian GDP growth by nearly 6 percent with a labour supply growth exceeding 6 percent from 2010 onward. The measures have a positive effect on the general government balance, improving the fiscal position over the long run by more than HUF 200 billion. The effects of the statutory changes are evaluated separately and cumulatively. The results of the study might be significantly influenced by the calibrated parameter values in the macromodel that is linked to the microsimulation.
    Keywords: behavioural microsimulation, micro macro model, taxation, transfers, tax reform
    JEL: C54 E62 H22 H31
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:mnb:opaper:2018/135&r=all
  10. By: Aguirregabiria, Victor; Marcoux, Mathieu
    Abstract: Imposing equilibrium restrictions provides substantial gains in the estimation of dynamic discrete games. Estimation algorithms imposing these restrictions -- MPEC, NFXP, NPL, and variations -- have different merits and limitations. MPEC guarantees local convergence, but requires the computation of high-dimensional Jacobians. The NPL algorithm avoids the computation of these matrices, but -- in games -- may fail to converge to the consistent NPL estimator. We study the asymptotic properties of the NPL algorithm treating the iterative procedure as performed in finite samples. We find that there are always samples for which the algorithm fails to converge, and this introduces a selection bias. We also propose a spectral algorithm to compute the NPL estimator. This algorithm satisfies local convergence and avoids the computation of Jacobian matrices. We present simulation evidence illustrating our theoretical results and the good properties of the spectral algorithm.
    Keywords: convergence; Convergence selection bias; Dynamic discrete games; Nested pseudo-likelihood; Spectral algorithms
    JEL: C13 C61 C73
    Date: 2019–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:14007&r=all
  11. By: Derek Singh; Shuzhong Zhang
    Abstract: This paper investigates calculations of robust CVA for OTC derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk can be characterized (and indeed quantified) via the robust CVA formulation. The simpler dual formulation of the robust CVA optimization is derived. Next, some computational experiments are conducted to measure the additional CVA charge due to distributional uncertainty under a variety of portfolio and market configurations. Finally some suggestions for future work, such as robust FVA, are discussed.
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1910.01781&r=all
  12. By: Derek Singh; Shuzhong Zhang
    Abstract: This paper investigates calculations of robust funding valuation adjustment (FVA) for over the counter (OTC) derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way funding risk can be characterized via the robust FVA formulation. The simpler dual formulation of the robust FVA optimization is derived. Next, some computational experiments are conducted to measure the additional FVA charge due to distributional uncertainty under a variety of portfolio and market configurations. Finally some suggestions for future work, such as robust capital valuation adjustment (KVA) and margin valuation adjustment (MVA), are discussed.
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1910.03993&r=all
  13. By: Aïd, René; Basei, Matteo; Callegaro, Giorgia; Campi, Luciano; Vargiolu, Tiziano
    Abstract: We consider a general nonzero-sum impulse game with two players. The main mathemat- ical contribution of the paper is a verification theorem which provides, under some regularity conditions, a suitable system of quasi-variational inequalities for the payoffs and the strate- gies of the two players at some Nash equilibrium. As an application, we study an impulse game with a one-dimensional state variable, following a real-valued scaled Brownian motion, and two players with linear and symmetric running payoffs. We fully characterize a family of Nash equilibria and provide explicit expressions for the corresponding equilibrium strategies and payoffs. We also prove some asymptotic results with respect to the intervention costs. Finally, we consider two further non-symmetric examples where a Nash equilibrium is found numerically.
    Keywords: stochastic differential game; impulse control; Nash equilibrium; quasi-variational inequality
    JEL: C1
    Date: 2019–07–17
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:100003&r=all

This nep-cmp issue is ©2019 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.