nep-cmp New Economics Papers
on Computational Economics
Issue of 2020‒10‒12
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  2. New Neighborhoods and an Iterated Local Search Algorithm for the Generalized Traveling Salesman Problem By Jeanette Schmidt; Stefan Irnich
  3. Uncertainty and Monetary Policy during Extreme Events By Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
  4. A Simulation Study of How Religious Fundamentalism Takes Root By Friedman, D.; Fan, J.; Gair, J.; Iyer, S.; Redlicki, B.; Velu, C.
  5. Forecasting impacts of Agricultural Production on Global Maize Price By Rotem Zelingher; David Makowski; Thierry Brunelle
  6. Forecasting recovery rates on non-performing loans with machine learning By Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
  7. Googling Unemployment During the Pandemic: Inference and Nowcast Using Search Data By Capema, Giulio; Colagrossi, Marco; Geraci, Andrea; Mazzarella, Gianluca
  8. Meta-learning approaches for recovery rate prediction By Gambetti, Paolo; Roccazzella, Francesco; Vrins, Frédéric
  9. Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011 By Cuong Nguyen; Ilan Noy; Dag Einar Sommervoll; Fang Yao
  10. Machine learning for optimizing complex site-specific management By Saikai, Yuji; Patel, Vivak; Mitchell, Paul
  11. Machine learning sentiment analysis, Covid-19 news and stock market reactions By Costola, Michele; Nofer, Michael; Hinz, Oliver; Pelizzon, Loriana
  12. A pandemic business interruption insurance By Alexis Louaas; Pierre Picard
  13. Exploring alternative approaches to estimate the impact of non-tariff measures and further implementation in simulation models By Ana Sanjuan Lopez; Marie-Luise Rau; Geert Woltjer
  14. Fuzzy DEA models for sports data analysis: The evaluation of the relative performances of professional (virtual) football teams By Pinto, Claudio
  15. The impact of land use effects in infrastructure appraisal By Eliasson, Jonas; Savemark, Christian; Franklin, Joel
  16. The Competitive Effects of Declining Entry Costs over Time: Evidence from the Static Random Access Memory Market By An-Hsiang Liu; Ralph Siebert

  1. By: Kenneth W. Clements (Economics Discipline, Business School, University of Western Australia); Marc Jim Mariano (KPMG Economics); George Verikios (KPMG Economics and Griffith University)
    Abstract: Foreign-domestic substitution elasticities (the so-called “Armington elasticities”) determine the degree of competitiveness in demand between similar products produced in different countries and are key parameters in a variety of numerical models of international trade. Armington elasticities are part of the explanation of the large increases in market shares of foreign products relative to locally produced ones in Australia, for example. The existing literature provides only limited evidence on these elasticities for Australia with the most disaggregated produced some time ago by Alaouze et al. (1977). This paper provides up-to-date parametric estimates of Armington elasticities for Australia with a reasonable degree of sectoral disaggregation. We use 22-years of data for 20 types of merchandise commodities, using OLS, panel and restricted-panel approaches. Our estimates range from 0.30 to 2.26, with higher elasticities for Transport and Equipment products and lower ones for Energy and. We illustrate the use of our elasticities with a trade-policy simulation using a computable generable equilibrium model of the Australian economy. We analyse the sensitivity of the results to the Armington elasticities by also using those estimated by Alaouze et al. (1977). We find an overestimation of economic effects when using the old Armington values.
    Keywords: Foreign-domestic substitution, Armington elasticities, CGE analysis, International trade, Tariff policy
    Date: 2020
  2. By: Jeanette Schmidt (Johannes Gutenberg University); Stefan Irnich (Johannes Gutenberg University)
    Abstract: The generalized traveling salesman problem (GTSP) is the problem of finding a cost-minimal cycle in a clustered graph so that exactly one vertex of every cluster is contained in the cycle. We introduce three new GTSP neighborhoods that allow the simultaneous permutation of the sequence of the clusters and the selection of vertices from each cluster. The three neighborhoods and some known neighborhoods from the literature are combined into a simple but effective iterated local search (ILS) for the GTSP. The simplicity of the ILS consists in its straightforward random neighborhood selection within the local search and an ordinary record-to-record ILS acceptance criterion. The computational experiments on four symmetric standard GTSP libraries show that, with some small refinements, the ILS can compete with state-of-the-art algorithms, although it is simple in structure and less involved to code compared to many other metaheuristics.
    Date: 2020–09–25
  3. By: Giovanni Pellegrino; Efrem Castelnuovo; Giovanni Caggiano
    Abstract: How damaging are uncertainty shocks during extreme events such as the great recession and the Covid-19 outbreak? Can monetary policy limit output losses in such situations? We use a nonlinear VAR framework to document the large response of real activity to a financial uncertainty shock during the great recession. We replicate this evidence with an estimated DSGE framework featuring a concept of uncertainty comparable to that in our VAR. We employ the DSGE model to quantify the impact on real activity of an uncertainty shock under different Taylor rules estimated with normal times vs. great recession data (the latter associated with a stronger response to output). We find that the uncertainty shock-induced output loss experienced during the 2007-09 recession could have been twice as large if policymakers had not responded aggressively to the abrupt drop in output in 2008Q3. Finally, we use our estimated DSGE framework to simulate different paths of uncertainty associated to different hypothesis on the evolution of the coronavirus pandemic. We find that: i) Covid-19-induced uncertainty could lead to an output loss twice as large as that of the great recession; ii) aggressive monetary policy moves could reduce such loss by about 50%.
    Keywords: house price prediction, machine learning, genetic algorithm, spatial aggregation
    JEL: G22 Q54 R11 R31
    Date: 2020
  4. By: Friedman, D.; Fan, J.; Gair, J.; Iyer, S.; Redlicki, B.; Velu, C.
    Abstract: Religious fundamentalism is observed across the world. We survey evidence on religious fundamentalism and then investigate its roots by reporting agent-based simulations of religiosity dynamics in a spatially dispersed population. Agents' religiosity responds to neighbors via direct interactions as well as via club goods effects. A simulation run is deemed fundamentalist if the final distribution contains a cohesive subset of agents with very high religiosity. We investigate whether such distributions are more prevalent when model parameters are shifted to reflect the transition from traditional societies to the modern world. The simulations suggest that the rise of fundamentalism in the modern world is aided by weaker attachment to the peer group, greater real income, and less substitutability between religious and secular goods, and arguably also by higher relative prices for secular goods and lower tolerance. Surprisingly, the current model suggests little role for the rise of long distance communication and transportation.
    Keywords: Fundamentalism, Club Goods, Agent-based Models
    JEL: Z12 D79 D85 H49
    Date: 2020–09–23
  5. By: Rotem Zelingher (ECO-PUB - Economie Publique - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); David Makowski; Thierry Brunelle (CIRED - Centre International de Recherche sur l'Environnement et le Développement - CNRS - Centre National de la Recherche Scientifique - ENPC - École des Ponts ParisTech - EHESS - École des hautes études en sciences sociales - AgroParisTech - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement)
    Abstract: Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand the origin of these shocks and anticipate their occurrence. In this study, we explore the possibility of predicting global prices of one of the world main agricultural commodity-maize-based on variations in regional production. We examine the performances of several machine-learning (ML) methods and compare them with a powerful time series model (TBATS) trained with 56 years of price data. Our results show that, out of nineteen regions, global maize prices are mostly influenced by Northern America. More specifically, small positive production changes relative to the previous year in Northern America negatively impact the world price while production of other regions have weak or no influence. We find that TBATS is the most accurate method for a forecast horizon of three months or less. For longer forecasting horizons, ML techniques based on bagging and gradient boosting perform better but require yearly input data on regional maize productions. Our results highlight the interest of ML for predicting global prices of major commodities and reveal the strong sensitivity of global maize price to small variations of maize production in Northern America.
    Keywords: Food-security,Maize,Agricultural commodity prices,Regional production,Machine learning
    Date: 2020–09–22
  6. By: Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
    Keywords: loss given default ; credit risk ; defaulted loans ; debt collection ; superior set of models
    Date: 2020–01–01
  7. By: Capema, Giulio (European Commission); Colagrossi, Marco (European Commission); Geraci, Andrea (European Commission); Mazzarella, Gianluca (European Commission)
    Abstract: The economic crisis caused by the covid-19 pandemic is unprecedented in recent history. We contribute to a growing literature investigating the economic consequences of covid-19 by showing how unemployment-related online searches across the EU27 reacted to the introduction of lock-downs. We exploit Google Trends topics to retrieve over two thousand search queries related to unemployment in 27 countries. We nowcast the monthly unemployment rate in the EU Member States to assess the relationship between search data and the underlying phenomenon as well as to identify the keywords that improve predictive accuracy. Drawing from this finding, we use the set of best predictors in a Difference-in-Differences framework to document a surge of unemploymentrelated searches in the wake of lock-downs of about 30%. This effect persists for more than five weeks. We suggest that the effect is most likely due to an increase in unemployment expectations.
    Keywords: Unemployment, nowcast, random forest, covid-19, Google Trends, Difference-in-Differences
    JEL: E24 C21 C53
    Date: 2020–09
  8. By: Gambetti, Paolo; Roccazzella, Francesco; Vrins, Frédéric
    Keywords: machine learning ; forecasts combination ; loss given default ; credit risk ; model risk
    Date: 2020–01–01
  9. By: Cuong Nguyen; Ilan Noy; Dag Einar Sommervoll; Fang Yao
    Abstract: On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.
    Keywords: house price prediction, machine learning, genetic algorithm, spatial aggregation
    JEL: G22 Q54 R11 R31
    Date: 2020
  10. By: Saikai, Yuji; Patel, Vivak; Mitchell, Paul
    Abstract: Despite the promise of precision agriculture for increasing the productivity by implementing site-specific management, farmers remain skeptical and its utilization rate is lower than expected. A major cause is a lack of concrete approaches to higher profitability. When involving many variables in both controlled management and monitored environment, optimal site-specific management for such high-dimensional cropping systems is considerably more complex than the traditional low-dimensional cases widely studied in the existing literature, calling for a paradigm shift in optimization of site-specific management. We propose an algorithmic approach that enables farmers to efficiently learn their own site-specific management through on-farm experiments. We test its performance in two simulated scenarios---one of medium complexity with 150 management variables and one of high complexity with 864 management variables. Results show that, relative to uniform management, site-specific management learned from 5-year experiments generates $43/ha higher profits with 25 kg/ha less nitrogen fertilizer in the first scenario and $40/ha higher profits with 55 kg/ha less nitrogen fertilizer in the second scenario. Thus, complex site-specific management can be learned efficiently and be more profitable and environmentally sustainable than uniform management.
    Keywords: Research and Development/Tech Change/Emerging Technologies
    Date: 2020–09–16
  11. By: Costola, Michele; Nofer, Michael; Hinz, Oliver; Pelizzon, Loriana
    Abstract: The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the "Natural Language Toolkit" that uses machine learning models to extract the news' sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms:, and Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
    Keywords: COVID-19 news,Sentiment Analysis,Stock Markets
    JEL: G10 G14 G15
    Date: 2020
  12. By: Alexis Louaas (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique); Pierre Picard (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique)
    Abstract: We analyze how pandemic business interruption coverage can be put in place by building on capitalization mechanisms. The pandemic risk cannot be mutualized since it affects simultaneously a large number of businesses, and furthermore, it has a systemic nature because it goes along with a severe decline in the real economy. However, as shown by COVID-19, pandemics affect economic sectors in a differentiated way: some of them are very severely affected because their activity is strongly impacted by travel bans and constraints on work organisation, while others are more resistant. This opens the door to risk coverage mechanisms based on a portfolio of financial securities, including long-short positions and options in stock markets. We show that such financial investment allow insurers to offer business interruption coverage in pandemic states, while simultaneously hedging the risks associated with the alternation of bullish and bearish non-pandemic states. These conclusions are derived from a theoretical model of corporate risk management, and they are illustrated by numerical simulations, using data from the French stock exchange.
    Keywords: pandemic,business interruption,insurance,risk management
    Date: 2020–09–17
  13. By: Ana Sanjuan Lopez (Unidade de Economía Agroalimentaria y de los Recursos Naturales, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA) Government of Aragon); Marie-Luise Rau; Geert Woltjer (Wageningen UR)
    Abstract: This report generates estimates for the effect of non-tariff measures (NTMs) on trade unit values. Adding to the latest development of the NTM analysis, we account for different types of NTMs for pairs of countries/regions. Our estimates thus provide new insights about the bilaterally distinct effect of specific NTMs. This is particularly interesting for policy makers that like to know which types of measures are relevant for trade from or to specific countries/regions and whether they are trade-hampering or trade-facilitating. We elaborate on the estimation of the price effect (measured in trade unit values) vis-Ã -vis the standard gravity on trade quantities (measured in trade value). A panel dataset (2012-2015) using the last releases of trade unit values (Berthou & Emlinger, 2011) and UNCTAD NTMs global database is built, and alternative approaches to account for the distinct bilateral impact are tested on beef, white meat (poultry) and milk. The focus is on trade between the EU member states and relevant regions with which the EU is negotiating or has just completed trade agreements: MERCOSUR, ASEAN, Japan and New Zealand. In this report, we do not implement the specific Ad-valorem equivalents (AVE) estimates for NTMs in a simulation model but rather provide a literature review that elaborates on the different approaches to depict NTMs in simulation models. The next step would be the application of the AVEs estimated in a simulation model in order to gauge the economy-wide effects of the respective NTMs under review.
    Keywords: non-tariff measures (NTMs), gravity, simulation models.
    Date: 2019–05
  14. By: Pinto, Claudio
    Abstract: The measurement of sports performances both of individual athletes and of an entire sports team, now highly widespread thanks to the enormous availability of sports data, is a crucial moment for professional sports clubs as the their survival is increasingly linked both to the results in the field obtained by its athletes and/or the team/s and to the achievement of many other sporting objectives. We here propose the use of the DEA methodology adapted to fuzzy logic to measure relative performances in the presence of uncertainty of a virtual sample of professional football teams along two dimensions: efficiency and effectiveness. The results obtained are especially interesting from the point of view of policy indications for the organization and management of the teams on the soccer pitch. The work then develops a second stage analysis structured in order to investigate on the one hand with the help of an econometric model the influence that a set of external factors can have on the performances and on the other, by calculating the gini coefficient, evaluates for various attitudes on the part of managers on uncertainty the degree of inequality in the distribution of sports performances of the groups that have participated in an ideal tournament. In conclusion, the work aims to develop, to our knowledge, an innovative and original way for the reference literature, a framework for analyzing sports data (and in particular for professional football clubs) in order to provide policy indications for improve their sports performances.
    Keywords: relative performance, sports data,fuzzy DEA
    JEL: C44 C55 D81 L25
    Date: 2020–09–27
  15. By: Eliasson, Jonas; Savemark, Christian; Franklin, Joel
    Abstract: When benefits of proposed infrastructure investments are forecasted, residential location is usually treated as fixed, since very few operational transport models are able to forecast residential relocation. It has been argued that this may constitute a source of serious error or bias when evaluating and comparing the benefits of proposed infrastructure investments. We use a stylized simulation model of a metropolitan region to compare calculated benefits for a large number of infrastructure investments with and without taking changes in residential location into account. In particular, we explore the changes in project selection when assembling an optimal project portfolio under a budget constraint. The simulation model includes endogenous land prices and demand for residential land, heterogeneous preferences and wage offers across residents, and spillover mechanisms which affect wage rates in zones. The model is calibrated to generate realistic travel patterns and demand elasticities. Our results indicate that ignoring residential relocation has a small but appreciable effect on the selected project portfolio, but only a very small effect on achieved total benefits.
    Keywords: Cost-benefit analysis, land use, wider impacts, land use/transport interaction models.
    JEL: R14 R40 R42
    Date: 2020
  16. By: An-Hsiang Liu; Ralph Siebert
    Abstract: We focus on the estimation of market entry costs that are declining over time and evaluate their impact on competition and market performance. We employ a dynamic oligopoly model in which firms make entry, exit, and production decisions in the presence of declining entry costs and learning by doing effects. Focusing on the static random access memory industry, we show that entry costs drastically decline by more than 80 percent throughout the life cycle. This corresponds to entry cost reductions of $30 million per quarter. To show the relevance of declining entry cost, we perform three counterfactuals in which a social planner can (a) regulate entry, (b) charge a tax on entry, and (c) provide a subsidy to promote entry. Our simulations show that declining entry costs can lead to excessive entry costs that result from too early entries by firms. Tax and entry regulation policy can reduce the excessive entry problem having a positive effect on total surplus while reducing consumer welfare. In contrast, a subsidy policy intensifies the problem of excessive entry at early periods but it increases consumer welfare.
    Keywords: dynamic efficiency gains, entry costs, entry protection, entry regulation, market entry, market structure, semiconductor industry, social planner, subsidies, taxes
    JEL: C10 L10 L60 O30
    Date: 2020

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