nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒04‒22
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The Impact of Interstate Mobility on the Effectiveness of Property Tax Reduction in Georgia By Andrew Feltenstein; Mark Rider; David L. Sjoquist; John V. Winters
  2. EU Merger Policy Predictability Using Random Forests By Pauline Affeldt
  3. Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models By Hieu Quang Nguyen; Abdul Hasib Rahimyar; Xiaodi Wang
  4. Universal features of price formation in financial markets: perspectives from Deep Learning By Justin Sirignano; Rama Cont
  5. Trade Protectionism and US Manufacturing Employment By Li, Chunding; Wang, Jing; Whalley, John
  6. Escape from model-land By Thompson, Erica L.; Smith, Leonard A.
  7. NIESIM: A Simulation-based Application for Estimating the Value of Information in Mobile Network Management By António Sousa Mendes; Pedro Godinho
  8. Deep-learning based numerical BSDE method for barrier options By Bing Yu; Xiaojing Xing; Agus Sudjianto
  9. A Weight-based Information Filtration Algorithm for Stock-Correlation Networks By Seyed Soheil Hosseini; Nick Wormald; Tianhai Tian
  10. Tax Morale and Perceived Intergenerational Mobility: a Machine Learning Predictive Approach By Caferra, Rocco; Morone, Andrea
  11. Collaboration and Delegation Between Humans and AI: An Experimental Investigation of the Future of Work By Fügener, A.; Grahl, J.; Gupta, A.; Ketter, W.
  12. How Effective Was the UK Carbon Tax? — A Machine Learning Approach to Policy Evaluation By Jan Abrell; Mirjam Kosch; Sebastian Rausch
  13. Two-phase heuristics for a multi-period capacitated facility location problem with service-differentiated customers By Sauvey, Christophe; Melo, Teresa; Correia, Isabel
  14. Choosing between Hail Insurance and Anti-Hail Nets: A Simple Model and a Simulation among Apples Producers in South Tyrol By Marco Rogna; Günter Schamel; Alex Weissensteiner

  1. By: Andrew Feltenstein (The Center for State and Local Finance, Georgia State University, USA); Mark Rider (The Center for State and Local Finance, Georgia State University, USA); David L. Sjoquist (The Center for State and Local Finance, Georgia State University, USA); John V. Winters (Iowa State University, USA)
    Abstract: This paper develop a computable general equilibrium (CGE) model and a microsimulation model (MSM) to analyze the economic and welfare effects of a Georgia propoerty tax proposal, which would have effectively eliminated school property taxes on homesteaded properties and replaced the lost revenue with a revenue-neutral increase in the state sales tax. Our CGE model, which is a modification of that used in Condon et al. (2015), explores the effects of significantly reducing or eliminating Georgia’s income tax and implementing a revenue-neutral increase in the state sales tax. This paper is set up as follows. We describe the Georgia proposal to reduce property taxes. Following that is a description of the CGE model, and a discussion of the outcomes of that model. The next section presents the MSM and its results. The last section concludes.
    Date: 2019–04
  2. By: Pauline Affeldt
    Abstract: I study the predictability of the EC’s merger decision procedure before and after the 2004 merger policy reform based on a dataset covering all affected markets of mergers with an official decision documented by DG Comp between 1990 and 2014. Using the highly flexible, non-parametric random forest algorithm to predict DG Comp’s assessment of competitive concerns in markets affected by a merger, I find that the predictive performance of the random forests is much better than the performance of simple linear models. In particular, the random forests do much better in predicting the rare event of competitive concerns. Secondly, postreform, DG Comp seems to base its assessment on a more complex interaction of merger and market characteristics than pre-reform. The highly flexible random forest algorithm is able to detect these potentially complex interactions and, therefore, still allows for high prediction precision.
    Keywords: Merger policy reform, DG Competition, Prediction, Random Forests
    JEL: K21 L40
    Date: 2019
  3. By: Hieu Quang Nguyen; Abdul Hasib Rahimyar; Xiaodi Wang
    Abstract: The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to predict future stock prices. Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.
    Date: 2019–04
  4. By: Justin Sirignano (UIUC - University of Illinois at Urbana Champaign - University of Illinois at Urbana-Champaign [Urbana]); Rama Cont (LPSM UMR 8001 - Laboratoire de Probabilités, Statistique et Modélisation - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model exhibits a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model — trained on data from all stocks — outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset-or sector-specific models as commonly done. Standard data normal-izations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecasting performance, showing evidence of path-dependence in price dynamics.
    Date: 2018–03–30
  5. By: Li, Chunding (China Agricultural University, Beijing); Wang, Jing (Western University (UWO)); Whalley, John (Western University (UWO))
    Abstract: This paper uses a numerical global general equilibrium model to simulate the possible effects of US initiated trade protection measures on US manufacturing employment. The simulation results show that US trade protection measures do not increase but will instead reduce manufacturing employment, and US losses will further increase if trade partners take retaliatory measures. The mechanism is that although the substitution effects between domestic and foreign goods have positive impacts, the substitution effects between manufacturing and service sectors and the retaliatory effects both have negative influences, therefore the whole effect is that the US will lose manufacturing employment.
    Keywords: Trade Protectionism, Manufacturing Employment, United States, Numerical Simulation JEL Classification: F16; C68; F62
    Date: 2019
  6. By: Thompson, Erica L.; Smith, Leonard A.
    Abstract: Both mathematical modelling and simulation methods in general have contributed greatly to understanding, insight and forecasting in many fields including macroeconomics. Never-theless, we must remain careful to distinguish model-land and model-land quantities from the real world. Decisions taken in the real world are more robust when informed by our best estimate of real-world quantities, than when "optimal" model-land quantities obtained from imperfect simulations are employed. The authors present a short guide to some of the temptations and pitfalls of model-land, some directions towards the exit, and two ways to escape.
    Keywords: modelling and simulation,decision-making,model evaluation,uncertainty,structural model error,dynamical systems,radical uncertainty
    JEL: C52 C53 C6 D8 D81
    Date: 2019
  7. By: António Sousa Mendes (Faculty of Economics, University of Coimbra); Pedro Godinho (CeBER and Faculty of Economics, University of Coimbra)
    Abstract: In this paper we introduce NIESIM (Network Information Economics SIMulation), a software for simulating a mobile communications scenario and studying the value of information in the context of mobile network management. The modelling principles and the simulation strategy used to design and develop NIESIM software are introduced, along with simulation results. We consider an application of NIESIM to support the definition of the grade of service in a network that is exposed to failures. An exploratory discussion of the findings and their implications to future work is also presented.
    Keywords: Telecommunications, Mobile Networks Management, Value of Information, Simulation.
    JEL: C63 D81 L96
    Date: 2019–04
  8. By: Bing Yu; Xiaojing Xing; Agus Sudjianto
    Abstract: As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation (BSDE). We can either solve the PDE to obtain option prices or solve its associated BSDE. Recently a deep learning technique has been applied to solve option prices using the BSDE approach. In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions. In this paper, we extend the deep-learning technique to solve a PDE with both terminal and boundary conditions. In particular, we will employ the technique to solve barrier options using Brownian motion bridges.
    Date: 2019–04
  9. By: Seyed Soheil Hosseini; Nick Wormald; Tianhai Tian
    Abstract: Several algorithms have been proposed to filter information on a complete graph of correlations across stocks to build a stock-correlation network. Among them the planar maximally filtered graph (PMFG) algorithm uses $3n-6$ edges to build a graph whose features include a high frequency of small cliques and a good clustering of stocks. We propose a new algorithm which we call proportional degree (PD) to filter information on the complete graph of normalised mutual information (NMI) across stocks. Our results show that the PD algorithm produces a network showing better homogeneity with respect to cliques, as compared to economic sectoral classification than its PMFG counterpart. We also show that the partition of the PD network obtained through normalised spectral clustering (NSC) agrees better with the NSC of the complete graph than the corresponding one obtained from PMFG. Finally, we show that the clusters in the PD network are more robust with respect to the removal of random sets of edges than those in the PMFG network.
    Date: 2019–04
  10. By: Caferra, Rocco; Morone, Andrea
    Abstract: The purpose of this paper is to investigate the linkage between the perceived intergenerational mobility and the preferences for tax payment. Unfortunately, we do not have a unique dataset, however missing data might be predicted by employing di�erent methods. We compare the efficiency of k-nearest-neighbors (kNN), Random Forest (RF) and Tobit-2-sample-2-Stage (T2S2S) techniques in predicting the perceived inter- generational mobility, hence we exploit the predicted values to estimate the relation with tax morale. Results provide evidence of a strong negative relation between perceived mobility and tax cheating, suggesting that fairness in tax payment has also to be seen on the light of the perceived efficiency of the welfare state in providing more opportunities across generations.
    Keywords: intergenerational mobility; tax morale; missing data;
    JEL: D63 I31 I32
    Date: 2019–04
  11. By: Fügener, A.; Grahl, J.; Gupta, A.; Ketter, W.
    Abstract: A defining question of our age is how AI will influence the workplace of the future and, thereby, the human condition. The dominant perspective is that the competition between AI and humans will be won by either humans or machines. We argue that the future workplace may not belong exclusively to humans or machines. Instead, it is better to use AI together with humans by combining their unique characteristics and abilities. In three experimental studies, we let humans and a state of the art AI classify images alone and together. As expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was inversion, where the AI delegated to a human when it was uncertain. Humans could in theory outperform all other configurations if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well; the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI. This result is novel and important for human-AI collaboration at the workplace. We believe it has broad implications for the future of work, the design of decision support systems, and management education in the age of AI.
    Keywords: Future of Work, Artificial Intelligence, Augmented Decision Environment, Deep Learning, Human-AI Collaboration, Machine Learning, Intelligent Software Agents
    Date: 2019–04–08
  12. By: Jan Abrell (ZHAW Winterthur and ETH Zurich, Switzerland); Mirjam Kosch (ZHAW Winterthur and ETH Zurich, Switzerland); Sebastian Rausch (ETH Zurich, Switzerland)
    Abstract: Carbon taxes are commonly seen as a rational policy response to climate change, but little is known about their performance from an ex-post perspective. This paper analyzes the emissions and cost impacts of the UK CPS, a carbon tax levied on all fossil-fired power plants. To overcome the problem of a missing control group, we propose a novel approach for policy evaluation which leverages economic theory and machine learning techniques for counterfactual prediction. Our results indicate that in the period 2013-2016 the CPS lowered emissions by 6.2 percent at an average cost of € 18 per ton. We find substantial temporal heterogeneity in tax-induced impacts which stems from variation in relative fuel prices. An important implication for climate policy is that a higher carbon tax does not necessarily lead to higher emissions reductions or higher costs.
    Keywords: Climate Policy, Carbon Tax, Carbon Pricing, Electricity, Coal, Natural Gas, United Kingdom, Carbon Price Surcharge, Policy Evaluation, Causal Inference, Machine Learning
    JEL: C54 Q48 Q52 Q58 L94
    Date: 2019–04
  13. By: Sauvey, Christophe; Melo, Teresa; Correia, Isabel
    Abstract: We investigate a recently introduced extension of the multi-period facility location problem that considers service-differentiated customer segments. Accordingly, some customers require their demands to be met on time, whereas the remaining customers accept delayed deliveries as long as lateness does not exceed a pre-specified threshold. In this case, late shipments can occur at most once over the delivery lead time, i.e. an order cannot be split over several time periods. At the beginning of the multi-period planning horizon, a number of facilities are in place with given capacities. A finite set of potential facility sites with multiple capacity levels is also available. The objective is to find the optimal locations and the opening, resp. closing, schedule for new, resp. existing, facilities that provide sufficient capacity to satisfy all customer demands at minimum cost. In this paper, we propose four heuristics that construct initial solutions to this problem and subsequently explore their neighborhoods via different local improvement mechanisms. Computational results with randomly generated instances demonstrate the effectiveness of the proposed heuristics. While a general-purpose mixed-integer programming solver fails to find feasible solutions to some instances within a given time limit, the heuristics provide good solutions to all instances already during the constructive phase and in significantly shorter computing times. During the improvement phase, the solution quality is further enhanced. For nearly one-fifth of the instances, the heuristic solutions outperform the best solutions identified by the solver.
    Keywords: facility location,multi-period,delivery lateness,constructive heuristics,local improvements
    Date: 2019
  14. By: Marco Rogna (Free University of Bolzano‐Bozen, Faculty of Economics and Management, Italy); Günter Schamel (Free University of Bolzano‐Bozen, Faculty of Economics and Management, Italy); Alex Weissensteiner (Free University of Bolzano‐Bozen, Faculty of Economics and Management, Italy)
    Abstract: There is a growing interest in analysing the diffusion of agricultural insurance, seen as an effective tool for managing farm risks. Much atten- tion has been dedicated to understanding the scarce adoption rate despite high levels of subsidization and policy support. In this paper, we analyse an aspect that seems to have been partially overlooked: the potential competing nature between insurance and other risk management tools. We consider hail as a single source weather shock and analyse the potential competing effect of anti-hail nets over insurance as instruments to cope with this shock by presenting a simple theoretical model that is rooted into expected utility theory. After describing the basic model, we perform some comparative static analysis to identify the role of individual elements that are shaping farmers' decisions. From this exercise it results that the worth of anti-hail nets compared to insurance is an increasing function of the overall risk of hail damages, of the farmers' level of risk aversion and of the worth of the agricultural output. Finally, we develop a simulation model using data related to apple production in South Tyrol, a Northern-Italian province with a relatively high risk of hail. The model generally confirms the results of the comparative static analysis and it shows that, in this region, anti-hail nets are often superior than insurance in expected utility terms.
    Keywords: Actuarial soundness, Agricultural insurance markets, Antihail nets, Hail, Expected utility
    JEL: Q12 Q18
    Date: 2019–04

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