nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒07‒17
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. ChatGPT Informed Graph Neural Network for Stock Movement Prediction By Zihan Chen; Lei Nico Zheng; Cheng Lu; Jialu Yuan; Di Zhu
  2. The Impact of Human-Artificial Intelligence Partnerships on Organizational Learning By Hendriks, Patrick; Sturm, Timo; Olt, Christian M.; Buxmann, Peter
  3. As Much Art as Science - Examining the Realization of Business Models Driven by Machine Learning Through a Dynamic Capabilities Perspective By Vetter, Oliver A.; Mehler, Maren F.; Buxmann, Peter
  4. Lie-detection algorithms attract few users but vastly increase accusation rates By von Schenk, Alicia; Klockmann, Victor; Bonnefon, Jean-François; Rahwan, Iyad; Köbis, Nils
  5. Lie-detection algorithms attract few users but vastly increase accusation rates By von Schenk, Alicia; Klockmann, Victor; Bonnefon, Jean-François; Rahwan, Iyad; Köbis, Nils
  6. Addressing Sample Selection Bias for Machine Learning Methods By Dylan Brewer; Alyssa Carlson
  7. Comparative Effectiveness of Machine Learning Methods for Causal Inference in Agricultural Economics By Badruddoza, Syed; Fuad, Syed; Amin, Modhurima D.
  8. Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models By Shadi Haj-Yahia; Omar Mansour; Tomer Toledo
  9. Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal By Kucklick; Priefer; Beverungen; Müller
  10. Combining machine learning and market integration to improve maize price predictions in sub-Saharan Africa By Anderson, Patrese; Baylis, Kathy; Davenport, Frank; Shukla, Shraddhanand
  11. Using Machine Learning to Construct Hedonic Price Indices By Michael Cafarella; Gabriel Ehrlich; Tian Gao; John C. Haltiwanger; Matthew D. Shapiro; Laura Zhao
  12. Maximally Machine-Learnable Portfolios By Philippe Goulet Coulombe; Maximilian Goebel
  13. Colombian inflation forecast using Long Short-Term Memory approach By Julián Alonso Cárdenas-Cárdenas; Deicy J. Cristiano-Botia; Nicolás Martínez-Cortés
  14. Ricardian Business Cycles By Lorenzo Bretscher; Jesús Fernández-Villaverde; Simon Scheidegger
  15. Dynamic Programming on a Quantum Annealer: Solving the RBC Model By Jesús Fernández-Villaverde; Isaiah J. Hull
  16. Deep Neural Network Estimation in Panel Data Models By Ilias Chronopoulos; Katerina Chrysikou; George Kapetanios; James Mitchell; Aristeidis Raftapostolos
  17. Layer: An Alternative Approach To Solve Large Capacitated Vehicle Routing Problem with Time Window Using AI and Exact Method By Mukherjee, Krishnendu
  18. How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing By Mehler, Maren F.; Vetter, Oliver A.
  19. Assessing the Economic Impact of Lockdowns in Italy: A Computational Input-Output Approach By Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni; Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano; Andrea Roventini
  20. Support Vector Machines and Bankruptcy Prediction By Zazueta, Jorge; Zazueta-Hernández, Jorge; Heredia, Andrea Chavez
  21. A Localized Neural Network with Dependent Data: Estimation and Inference By Jiti Gao; Bin Peng; Yanrong Yang
  22. Internal meta-analysis for Monte Carlo simulations By Andor, Mark Andreas; Bernstein, David H.; Parmeter, Christopher F.; Sommer, Stephan
  23. Open Science vs. Mission-oriented Policies and the Long-run Dynamics of Integrated Economies: An Agent-based Model with a Kaldorian Flavour. By Andrea Borsato; Andre Lorentz
  24. Informal employment from migration shocks By Marica Valente; Timm Gries; Lorenzo Trapani
  25. Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects By Simon Hirsch; Florian Ziel

  1. By: Zihan Chen; Lei Nico Zheng; Cheng Lu; Jialu Yuan; Di Zhu
    Abstract: ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.03763&r=cmp
  2. By: Hendriks, Patrick; Sturm, Timo; Olt, Christian M.; Buxmann, Peter
    Abstract: To make sense of their increasingly digital and complex environments, organizations strive for a future in which machine learning (ML) systems join humans in collaborative learning partnerships to complement each other’s learning capabilities. While these so-called artificial assistants enable their human partners (and vice versa) to gain insights about unique knowledge domains that would otherwise remain hidden from them, they may also disrupt and impede each other's learning. To explore the virtuous and vicious dynamics that affect organizational learning, we conduct a series of agent-based simulations of different learning modes between humans and artificial assistants in an organization. We find that aligning the learning of humans and artificial assistants and allowing them to influence each other’s learning processes equally leads to the highest organizational performance.
    Date: 2023–06–16
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:138376&r=cmp
  3. By: Vetter, Oliver A.; Mehler, Maren F.; Buxmann, Peter
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:138319&r=cmp
  4. By: von Schenk, Alicia; Klockmann, Victor; Bonnefon, Jean-François; Rahwan, Iyad; Köbis, Nils
    Abstract: People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations — both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence (AI). Will people elect to use lie-detection AI that outperforms humans, and if so, will they show less restraint in their accusations? To find out, we built a machine learning classifier whose accuracy (66.86%) was significantly better than human accuracy (46.47%) lie-detection task. We conducted an incentivized lie-detection experiment (N = 2040) in which we measured participants’ propensity to use the algorithm, as well as the impact of that use on accusation rates and accuracy. Our results reveal that (a) requesting predictions from the lie-detection AI and especially (b) receiving AI predictions that accuse others of lying increase accusation rates. Due to the low uptake of the algorithm (31.76% requests), we do not see an improvement in accuracy when the AI prediction becomes available for purchase.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:128163&r=cmp
  5. By: von Schenk, Alicia; Klockmann, Victor; Bonnefon, Jean-François; Rahwan, Iyad; Köbis, Nils
    Abstract: People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations — both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence (AI). Will people elect to use lie-detection AI that outperforms humans, and if so, will they show less restraint in their accusations? To find out, we built a machine learning classifier whose accuracy (66.86%) was significantly better than human accuracy (46.47%) lie-detection task. We conducted an incentivized lie-detection experiment (N = 2040) in which we measured participants’ propensity to use the algorithm, as well as the impact of that use on accusation rates and accuracy. Our results reveal that (a) requesting predictions from the lie-detection AI and especially (b) receiving AI predictions that accuse others of lying increase accusation rates. Due to the low uptake of the algorithm (31.76% requests), we do not see an improvement in accuracy when the AI prediction becomes available for purchase.
    Date: 2023–06–21
    URL: http://d.repec.org/n?u=RePEc:tse:iastwp:128164&r=cmp
  6. By: Dylan Brewer (Georgia Institute of Technology); Alyssa Carlson (Department of Economics, University of Missouri)
    Abstract: We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine-learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsens sample selection bias. We propose two control-function approaches that remove the effects of selection bias before training and find that they reduce mean-squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re-election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used.
    Keywords: sample selection, machine learning, control function, inverse probability weighting
    JEL: C13 C31 C55 D72
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:umc:wpaper:2310&r=cmp
  7. By: Badruddoza, Syed; Fuad, Syed; Amin, Modhurima D.
    Keywords: Research Methods/Statistical Methods, Food Consumption/Nutrition/Food Safety, Agricultural and Food Policy
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335782&r=cmp
  8. By: Shadi Haj-Yahia; Omar Mansour; Tomer Toledo
    Abstract: Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.00016&r=cmp
  9. By: Kucklick (Paderborn University); Priefer (Paderborn University); Beverungen (Paderborn University); Müller (Paderborn University)
    Abstract: Information systems have proven their value in facilitating pricing decisions. Still, predicting prices for complex goods remains challenging due to information asymmetries. Beyond Search qualities that sellers can identify ex-ante of a purchase, these goods possess Experience qualities only identifiable ex-post. While research has discussed how information asymmetries cause market failure, it remains unclear what benefits Search and Experience qualities offer for information systems that enable pricing on online markets. In a Machine Learning-based study, we quantify their predictive power for online real estate pricing. We use Geographic Information Systems and Computer Vision to incorporate spatial and image data into a Machine Learning algorithm for price prediction. We find that these secondary use data can transform Experience qualities to Search qualities, increasing the predictive power by up to 15.4%. Our results suggest that secondary use data can provide valuable resources for improving the predictive power of pricing complex goods.
    Keywords: information asymmetries, real estate appraisal; SEC theory; Machine Learning; Geographic Information Systems, Computer Vision
    JEL: C45 R32 R00
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:pdn:dispap:112&r=cmp
  10. By: Anderson, Patrese; Baylis, Kathy; Davenport, Frank; Shukla, Shraddhanand
    Keywords: International Development, Marketing, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335809&r=cmp
  11. By: Michael Cafarella; Gabriel Ehrlich; Tian Gao; John C. Haltiwanger; Matthew D. Shapiro; Laura Zhao
    Abstract: This paper uses machine learning (ML) to estimate hedonic price indices at scale from item-level transaction and product characteristics. The procedure uses state-of-the-art approaches from hedonic econometrics and implements them with a neural network ML approach. Applying the methodology to Nielsen Retail Scanner data leads to a large hedonic adjustment to the Tornqvist index for food product groups: Cumulative food inflation over the period from 2007 through 2015 is reduced by half from 5.9% to 2.8% -- owing to quality adjustment. These results suggest that quality improvement via product turnover is important even in product groups that are not normally considered to feature rapid technological progress. The approach in the paper thus demonstrates the feasibility and importance of implementing hedonic adjustment at scale.
    JEL: C81 E31
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31315&r=cmp
  12. By: Philippe Goulet Coulombe; Maximilian Goebel
    Abstract: When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay's original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.05568&r=cmp
  13. By: Julián Alonso Cárdenas-Cárdenas; Deicy J. Cristiano-Botia; Nicolás Martínez-Cortés
    Abstract: We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards. **** RESUMEN: A través de dos enfoques utilizamos redes neuronales Long Short-Term Memory (LSTM), una técnica de aprendizaje profundo, para pronosticar la inflación en Colombia con un horizonte de doce meses. El primer enfoque emplea solo información de la variable objetivo, la inflación, mientras que el segundo incorpora información adicional proveniente de algunas variables relevantes. Utilizamos rolling sample dentro del proceso tradicional de construcción de las redes neuronales, seleccionando los hiperparámetros con criterios de minimización del error de pronóstico. Nuestros resultados muestran una mejor capacidad de pronóstico de la red bajo el segundo enfoque, superando al primer enfoque y a modelos ARIMA optimizados para pronóstico (con y sin variables explicativas). Esta mejora en la capacidad de pronóstico es más pronunciada en horizontes más largos, específicamente entre el séptimo y doceavo mes.
    Keywords: Deep learning, Long Short Term Memory neural networks, forecast, inflation, Aprendizaje profundo, redes neuronales Long Short-Term Memory, pronóstico, inflación
    JEL: C45 C51 C52 C53 C61 E37
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:bdr:borrec:1241&r=cmp
  14. By: Lorenzo Bretscher (University of Lausanne; Swiss Finance Institute, and CEPR); Jesús Fernández-Villaverde (University of Pennsylvania; National Bureau of Economic Research (NBER)); Simon Scheidegger (University of Lausanne)
    Abstract: This paper presents a dynamic stochastic general equilibrium model of Ricardian business cycles. Our model is Ricardian because countries (or, equivalently, regions) trade to take advantage of their comparative advantages. Their relative efficiencies, however, change over time stochastically. Similarly, country-specific shocks to demand, supply, and investment efficiency induce countries to engage in intra- and intertemporal substitutions in non-durable consumption, investment, services, and trade, generating business cycles. Finally, all agents have rational expectations about the stochastic components of the model. We solve the model globally using deep neural networks and calibrate it to the U.S., Europe, and China. Our quantitative results highlight the role of trading costs in shaping the responses of the economy to different shocks.
    Keywords: International Trade, Business Cycles, General Equilibrium, Comparative Advantage, Deep Learning
    JEL: C45 C63 F10 F40
    Date: 2023–01
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2343&r=cmp
  15. By: Jesús Fernández-Villaverde; Isaiah J. Hull
    Abstract: We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.
    JEL: C63 C78 E37
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31326&r=cmp
  16. By: Ilias Chronopoulos; Katerina Chrysikou; George Kapetanios; James Mitchell; Aristeidis Raftapostolos
    Abstract: In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time series models. Containment or lockdown policies, as instigated at the national-level by governments, are found to have out-of-sample predictive power for new COVID-19 cases. We illustrate how the use of partial derivatives can help open the "black-box" of neural networks and facilitate semi-structural analysis: school and workplace closures are found to have been effective policies at restricting the progression of the pandemic across the G7 countries. But our methods illustrate significant heterogeneity and time-variation in the effectiveness of specific containment policies.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.19921&r=cmp
  17. By: Mukherjee, Krishnendu
    Abstract: To the best of my knowledge, this problem has never been addressed by any researcher. This paper studies the effect of K-means, the Gaussian Mixture Model (GMM), and the integrated use of autoencoder and K-means on the computational time, MIP gap, feasible route, subtour, and the optimum use of vehicles. Miller-Tucker-Zemlin (MTZ) subtour elimination constraint is considered in this regard. This paper also gives the concept of a “layer”, which could be effective to solve a large vehicle routing problem with a time window (VRPTW) quickly.
    Keywords: Machine Learning, Deep Learning, Mixed Integer Linear Program, and Large VRPTW
    JEL: C6 C61 C63
    Date: 2023–06–03
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117513&r=cmp
  18. By: Mehler, Maren F.; Vetter, Oliver A.
    Abstract: Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research.
    Date: 2023–06–14
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:138317&r=cmp
  19. By: Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)
    Abstract: We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown.
    Keywords: Input-output, Covid-19, Lockdown, Italy
    Date: 2021–10
    URL: http://d.repec.org/n?u=RePEc:hal:spmain:hal-04103906&r=cmp
  20. By: Zazueta, Jorge; Zazueta-Hernández, Jorge; Heredia, Andrea Chavez
    Abstract: We provide an intuitive construction of a support vector machine (SVM) and explore the motivation behind using different tools for data classification. Beginning with linear classifiers, we build intuition on the subtlety of classification in increasingly non-linear circumstances and conclude with an example of bankruptcy prediction to illustrate the effectiveness and flexibility of support vector machines.
    Date: 2023–06–23
    URL: http://d.repec.org/n?u=RePEc:osf:socarx:7z24k&r=cmp
  21. By: Jiti Gao; Bin Peng; Yanrong Yang
    Abstract: In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes. We explore the use of identification restrictions from a nonparametric regression perspective, and establish an estimation theory for the LNN setting under a set of mild conditions. The asymptotic distributions are derived accordingly, and we show that LNN automatically eliminates the dependence of data when calculating the asymptotic variances. The finding is important, as one can easily use different types of wild bootstrap methods to obtain valid inference practically. In particular, for quantitative outcomes, the proposed LNN approach yields closed-form expressions for the estimates of some key estimators of interest. Last but not least, we examine our theoretical findings through extensive numerical studies.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.05593&r=cmp
  22. By: Andor, Mark Andreas; Bernstein, David H.; Parmeter, Christopher F.; Sommer, Stephan
    Abstract: Monte Carlo (MC) simulations are one of the dominant approaches to compare statistical methods. To date, there is no standard procedure for MC simulations. Although internally valid, they exhibit a certain degree of arbitrariness through the various choices that researchers make. In this paper, we propose the use of an internal meta-analysis for MC simulations to allow a standardized analysis, synthesis and presentation of MC simulation results in a transparent manner. The use of an internal meta-analysis allows (i) a much more standardized procedure and (ii) comprehensive analysis of a large variety and number of simulations. To exemplify the procedure, we conduct an extensive set of simulations to compare the empirical performance of three different estimators of the generalized stochastic frontier panel data model. Besides contributing to the literature on efficiency analysis by improving the understanding of the merits of the three different estimators, we demonstrate the applicability and usefulness of internal meta-analysis for MC simulations in general.
    Keywords: Monte Carlo simulation, meta-analysis, stochastic frontier analysis, productionfunction, panel data
    JEL: C1 C15
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:zbw:rwirep:997&r=cmp
  23. By: Andrea Borsato; Andre Lorentz
    Abstract: This paper offers a contribution to the literature on science policies and on the possible trade-off that might arise between broad spectrum science-technology policies and missionoriented programs. We develop a multi-country, multi-sectoral agent-based model of economic dynamics with endogenous structural change that represents a small-scale monetary union. Findings are threefold. Firstly, science policies from national governments, even when symmetric, act as a source of growth divergence across countries. Secondly, even if economic growth is largely driven by the sectors with absolute advantages, having at least a little flow of open science investments is sufficient for the other industries to survive and innovate, hence preserving the bio-diversity of the economic structure. Thirdly, science policy alone is a sufficient means to break monopolistic tendencies, trigger competition and reduce income inequality. Still, such results are conditioned to the flow of open science. Yet, the working of the model suggests that supply-side science policies should be paired with demand-side policies for the wide re-organisation of consumption habits, if grand societal challenges are to be met.
    Keywords: Science policies, Structural and technical change, Economic growth.
    JEL: E11 E32 O33 O41
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ulp:sbbeta:2023-17&r=cmp
  24. By: Marica Valente; Timm Gries; Lorenzo Trapani
    Abstract: We propose a new approach to detect and quantify informal employment resulting from irregular migration shocks. Focusing on a largely informal sector, agriculture, and on the exogenous variation from the Arab Spring wave on southern Italian coasts, we use machine-learning techniques to document abnormal increases in reported (vs. predicted) labor productivity on vineyards hit by the shock. Misreporting is largely heterogeneous across farms depending e.g. on size and grape quality. The shock resulted in a 6% increase in informal employment, equivalent to one undeclared worker for every three farms on average and 23, 000 workers in total over 2011-2012. Misreporting causes significant increases in farm profits through lower labor costs, while having no impact on grape sales, prices, or wages of formal workers.
    Keywords: Informal employment, Migration shocks, Farm labor, Machine learning
    JEL: F22 J61 J43 J46 C53
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:inn:wpaper:2023-09&r=cmp
  25. By: Simon Hirsch; Florian Ziel
    Abstract: Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.13419&r=cmp

This nep-cmp issue is ©2023 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.