nep-cmp New Economics Papers
on Computational Economics
Issue of 2020‒11‒02
eight papers chosen by
Stan Miles
Thompson Rivers University

  1. COVID-Town: An Integrated Economic-Epidemiological Agent-Based Model By Mellacher, Patrick
  2. Assessing the Scoreboard of the EU Macroeconomic Imbalances Procedure: (Machine) Learning from Decisions By João Amador; Tiago Alves; Francisco Gonçalves
  3. Aggregating Heterogeneous-Agent Models with Permanent Income Shocks By Harmenberg, Karl
  4. Data science in economics: comprehensive review of advanced machine learning and deep learning methods By Nosratabadi, Saeed; Mosavi, Amir; Duan, Puhong; Ghamisi, Pedram; Filip, Ferdinand; Band, Shahab S.; Reuter, Uwe; Gama, Joao; Gandomi, Amir H.
  5. microWELT: Microsimulation Projection of Indicators of the Economic Effects of Population Ageing Based on Disaggregated National Transfer Accounts By Martin Spielauer; Thomas Horvath; Marian Fink; Gemma Abio; Guadalupe Souto Nieves; Concepció Patxot; Tanja Istenic
  6. AAMDRL: Augmented Asset Management with Deep Reinforcement Learning By Eric Benhamou; David Saltiel; Sandrine Ungari; Abhishek Mukhopadhyay; Jamal Atif
  7. microWELT: Socio-Demographic Parameters and Projections for Austria, Spain, Finland, and the UK By Martin Spielauer; Thomas Horvath; Walter Hyll; Marian Fink
  8. Reassessing the Resource Curse using Causal Machine Learning By Roland Hodler; Michael Lechner; Paul A. Raschky

  1. By: Mellacher, Patrick
    Abstract: I develop a novel macroeconomic epidemiological agent-based model to study the impact of the COVID-19 pandemic under varying policy scenarios. Agents differ with regard to their profession, family status and age and interact with other agents at home, work or during leisure activities. The model allows to implement and test actually used or counterfactual policies such as closing schools or the leisure industry explicitly in the model in order to explore their impact on the spread of the virus, and their economic consequences. The model is calibrated with German statistical data on demography, households, firm demography, employment, company profits and wages. I set up a baseline scenario based on the German containment policies and fit the epidemiological parameters of the simulation to the observed German death curve and an estimated infection curve of the first COVID-19 wave. My model suggests that by acting one week later, the death toll of the first wave in Germany would have been 180% higher, whereas it would have been 60% lower, if the policies had been enacted a week earlier. I finally discuss two stylized fiscal policy scenarios: procyclical (zero-deficit) and anticyclical fiscal policy. In the zero-deficit scenario a vicious circle emerges, in which the economic recession spreads from the high-interaction leisure industry to the rest of the economy. Even after eliminating the virus and lifting the restrictions, the economic recovery is incomplete. Anticyclical fiscal policy on the other hand limits the economic losses and allows for a V-shaped recovery, but does not increase the number of deaths. These results suggest that an optimal response to the pandemic aiming at containment or “holding out for a vaccine” combines early introduction of containment measures to keep the number of infected low with expansionary fiscal policy to keep output in lower risk sectors high.
    Keywords: Agent-based model, economic epidemiology, covid-19, pandemic
    JEL: C63 E17 H12 H30 I18 L83
    Date: 2020–10–19
  2. By: João Amador; Tiago Alves; Francisco Gonçalves
    Abstract: This paper uses machine learning methods to identify the macroeconomic variables that are most relevant for the classification of countries along the categories of the EU Macroeconomic Imbalances Procedure (MIP). The random forest algorithm considers the 14 headline indicators of the MIP scoreboard and the set of past decisions taken by the European Commission when classifying countries along the macroeconomic imbalances categories. The algorithm identifies the current account balance, the net international investment position and the unemployment rate as key variables, mostly to classify countries that need corrective action, notably through economic adjustment programmes.
    JEL: C40 F15
    Date: 2020
  3. By: Harmenberg, Karl (Department of Economics, Copenhagen Business School)
    Abstract: I introduce a method for simulating aggregate dynamics of heterogeneous-agent models where log permanent income follows a random walk. The idea is to simulate the model using a counterfactual permanent-income-neutral measure which incorporates the effect that permanent income shocks have on macroeconomic aggregates. With the permanent-income-neutral measure, one does not need to keep track of the permanent-income distribution. The permanent-income-neutral measure is both useful for the analytical characterization of aggregate consumption-savings behavior and for simulating numerical models. Furthermore, it is trivial to implement with a few lines of code.
    Keywords: Permanent income; Consumption; Simulation
    JEL: C63 E21 E27
    Date: 2020–09–21
  4. By: Nosratabadi, Saeed; Mosavi, Amir; Duan, Puhong; Ghamisi, Pedram; Filip, Ferdinand; Band, Shahab S.; Reuter, Uwe; Gama, Joao; Gandomi, Amir H.
    Abstract: This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
    Date: 2020–10–16
  5. By: Martin Spielauer (WIFO); Thomas Horvath; Marian Fink; Gemma Abio; Guadalupe Souto Nieves; Concepció Patxot; Tanja Istenic
    Abstract: This paper studies how changes in the population composition by education and family characteristics impact on indicators of the economic effects of population ageing based on National Transfer Accounts (NTAs). NTAs constitute cross-sectional per-capita age-profiles of the key variables of national accounts consumption, income, saving, and public transfers, incorporating an estimation of private transfers. A variety of indicators based on NTA data combined with population projections was developed in the literature, of which we have selected two for our analysis: the Support Ratio (SR) and the Impact Index (IMP). We complement existing projections by using new disaggregated NTA data by education and family type, contrasting the results to the same indicators based on NTAs by age. Our projection analysis is performed using the dynamic microsimulation model microWELT. The model provides the required detailed socio-demographic projections and incorporates the NTA accounting framework. Our results show that indicators based on disaggregated data can give a very distinct picture of the economic effects of population ageing, as the burden of ageing is alleviated by the education expansion. Our study compares results for Austria and Spain.
    Keywords: Microsimulation, Education, Demographic Change, National Transfer Accounts
    Date: 2020–10–20
  6. By: Eric Benhamou; David Saltiel; Sandrine Ungari; Abhishek Mukhopadhyay; Jamal Atif
    Abstract: Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how Deep Reinforcement Learning (DRL) can tackle this challenge. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) the impact of a one period lag between observations and actions that is more realistic for an asset management environment, (iii) the implementation of a new repetitive train test method called walk forward analysis, similar in spirit to cross validation for time series. Although our experiment is on trading bots, it can easily be translated to other bot environments that operate in sequential environment with regime changes and noisy data. Our experiment for an augmented asset manager interested in finding the best portfolio for hedging strategies shows that AAMDRL achieves superior returns and lower risk.
    Date: 2020–09
  7. By: Martin Spielauer (WIFO); Thomas Horvath; Walter Hyll; Marian Fink
    Abstract: The aim of this paper is twofold: First, it provides an overview of the socio-demographic core modules of the dynamic microsimulation model microWELT. Second, it describes the essential socio-demographic characteristics of four European countries – Austria, Spain, Finland, and UK as representatives of four welfare state regimes (conservative, mediterranean, universalistic, and liberal) – and the processes that drive socio-demographic change which we aim at capturing with the model. MicroWELT is developed as a tool for the comparative study of the distributional effects of four welfare state regimes, represented by the four studied countries. Processes with potential links to welfare state types include 1. the intergenerational transmission of education, 2. childlessness and fertility by education, 3. partnership behaviours and lone parenthood, 4. age at leaving home, and 5. mortality differentials by sex and education. Through microWELT projections, we identify the impact of these processes on the future population composition by age, sex, education, and family characteristics of the studied countries. This paper is part of a series of related papers and other resources which together build comprehensive documentation and presentation of the research performed developing and using microWELT. All materials are available at the project website
    Keywords: Dynamic Microsimulation, Demographic Change, Welfare State Regimes
    Date: 2020–10–13
  8. By: Roland Hodler (SoDa Laboratories, Monash University); Michael Lechner (SoDa Laboratories, Monash University); Paul A. Raschky (SoDa Laboratories, Monash University)
    Abstract: We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict. Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices.
    Keywords: resource curse, economic development, conflict, causal machine learning, Africa
    JEL: C21 O13 O55 Q34 R12
    Date: 2020–09

This nep-cmp issue is ©2020 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.
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