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  1. Vorhersage des in-game Status im Fußball mit Maschinellem Lernen basierend auf zeitkontinuierlichen Spielerpositionsdaten
    Erschienen: 2022
    Verlag:  Technische Universität Chemnitz, Chemnitz

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    Sprache: Deutsch
    Medientyp: Buch (Monographie)
    Format: Online
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    Schlagworte: Fußball; Maschinelles Lernen; Computerspiel; Datenanalyse; Fußball; Maschinelles Lernen; Position
    Weitere Schlagworte: Fußball; Maschinelles Lernen; Spielerpositionsdaten; in-game Status; soccer; machine learning; player position data; in-game status
    Umfang: Online-Ressource
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    In: spinfortec2022. Chemnitz, 29. - 30. September 2022

  2. Choosing between explicit cartel formation and tacit collusion
    an experiment
    Erschienen: 2020
    Verlag:  Universität Potsdam, Potsdam

    Numerous studies investigate which sanctioning institutions prevent cartel formation but little is known as to how these sanctions work. We contribute to understanding the inner workings of cartels by studying experimentally the effect of sanctioning... mehr

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    Numerous studies investigate which sanctioning institutions prevent cartel formation but little is known as to how these sanctions work. We contribute to understanding the inner workings of cartels by studying experimentally the effect of sanctioning institutions on firms’ communication. Using machine learning to organize the chat communication into topics, we find that firms are significantly less likely to communicate explicitly about price fixing when sanctioning institutions are present. At the same time, average prices are lower when communication is less explicit. A mediation analysis suggests that sanctions are effective in hindering cartel formation not only because they introduce a risk of being fined but also by reducing the prevalence of explicit price communication.

     

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    Auflage/Ausgabe: This version: August 3, 2020
    Schriftenreihe: CEPA discussion papers ; No. 19
    Schlagworte: cartel; collusion; communication; experiment; machine learning
    Umfang: 1 Online-Ressource (55 Seiten, 843 KB), Illustrationen, Diagramme
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    Literaturverzeichnis: Seite 26-31

  3. A machine learning approach to volatility forecasting
    Erschienen: [2021]
    Verlag:  Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark

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    Schriftenreihe: CREATES research paper ; 2021, 03
    Schlagworte: Gradient boosting; high-frequency data; machine learning; neural network; random forest; realized variance; regularization; volatility forecasting
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  4. Transnational machine learning with screens for flagging bid-rigging cartels
    Erschienen: 2020
    Verlag:  University of Fribourg, Switzerland, Faculty of Economics and Social Sciences, Fribourg

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    Schriftenreihe: Working papers SES / Université de Fribourg, Faculté des sciences economiques et sociales ; n. 519 (10.2020)
    Schlagworte: Bid rigging; screening methods; machine learning; random forest; ensemble methods
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  5. The impact of VC on the exit and innovation outcomes of EIF-backed start-ups
    Erschienen: February 2021
    Verlag:  European Investment Fund, Luxembourg

    We use competing risks methods to investigate the causal link between venture capital (VC) investments supported by the EIF and the exit prospects and patenting activity of young and innovative firms. Using a novel dataset covering European start-ups... mehr

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    We use competing risks methods to investigate the causal link between venture capital (VC) investments supported by the EIF and the exit prospects and patenting activity of young and innovative firms. Using a novel dataset covering European start-ups receiving VC financing in the years 2007 to 2014, we generate a counterfactual group of non-VC-backed young and innovative firms via a combination of exact and propensity score matching. To offset the limited set of observables allowed by our data, we introduce novel measures based on machine learning, network theory, and satellite imagery analysis to estimate treatment propensity. Our estimates indicate that start-ups receiving EIF VC experienced a significant threefold increase in their likelihood to exit via M&A. We find a similarly large effect in the case of IPO, albeit only weakly significant. Moreover, we find that EIF VC contributed to a 13 percentage points higher incidence in patenting activity during the five years following the investment date. Overall, our work provides meaningful evidence towards the positive effects of EIF’s VC activity on the exit prospects and innovative capacity of young and innovative businesses in Europe.

     

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    hdl: 10419/231445
    Schriftenreihe: The European venture capital landscape ; volume 6
    Working paper / EIF research & market analysis ; 70 (2021)
    Schlagworte: EIF; venture capital; public intervention; exit strategy; innovation; start-ups; machine learning; geospatial analysis; network theory
    Umfang: 1 Online-Ressource (circa 60 Seiten), Illustrationen
  6. Trade sentiment and the stock market
    new evidence based on big data textual analysis of Chinese media
    Erschienen: 2021
    Verlag:  Bank for International Settlements, Monetary and Economic Department, [Basel]

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    Schriftenreihe: BIS working papers ; no 917 (January 2021)
    Schlagworte: Stock returns; trade; sentiment; big data; neural network; machine learning
    Umfang: 1 Online-Ressource (circa 48 Seiten), Illustrationen
  7. Big data and machine learning in central banking
    Erschienen: 2021
    Verlag:  Bank for International Settlements, Monetary and Economic Department, [Basel]

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    Schriftenreihe: BIS working papers ; no 930 (March 2021)
    Schlagworte: big data; central banks; machine learning; artificial intelligence; data science
    Umfang: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  8. Understanding the performance of machine learning models to predict credit default
    a novel approach for supervisory evaluation
    Erschienen: 2021
    Verlag:  Banco de España, Madrid

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    Schriftenreihe: Documentos de trabajo / Banco de España, Eurosistema ; no. 2105
    Schlagworte: machine learning; credit risk; prediction; probability of default; IRB system
    Umfang: 1 Online-Ressource (circa 44 Seiten), Illustrationen
  9. The value of data for prediction policy problems
    evidence from antibiotic prescribing
    Erschienen: 2021
    Verlag:  DIW Berlin, German Institute for Economic Research, Berlin

    Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the... mehr

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    Large-scale data show promise to provide efficiency gains through individualized risk predictions in many business and policy settings. Yet, assessments of the degree of data-enabled efficiency improvements remain scarce. We quantify the value of the availability of a variety of data combinations for tackling the policy problem of curbing antibiotic resistance, where the reduction of inefficient antibiotic use requires improved diagnostic prediction. Fousing on antibiotic prescribing for suspected urinary tract infections in primary care in Denmark, we link individual-level administrative data with microbiological laboratory test outcomes to train a machine learning algorithm predicting bacterial test results. For various data combinations, we assess out of sample prediction quality and efficiency improvements due to prediction-based prescription policies. The largest gains in prediction quality can be achieved using simple characteristics such as patient age and gender or patients’ health care data. However, additional patient background data lead to further incremental policy improvements even though gains in prediction quality are small. Our findings suggest that evaluating prediction quality against the ground truth only may not be sufficient to quantify the potential for policy improvements.

     

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    hdl: 10419/233050
    Schriftenreihe: Discussion papers / Deutsches Institut für Wirtschaftsforschung ; 1939
    Schlagworte: prediction policy; data combination; machine learning; antibiotic prescribing
    Umfang: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  10. Two-stage least squares random forests with a replication of angrist and evans (1998)
    Erschienen: August 2020
    Verlag:  Verein für Socialpolitik, [Köln]

    We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to... mehr

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    We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental variables in Angrist and Evans (Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size, American Economic Review, Vol. 88, 1998). The two-stage least squares random forest allows one to investigate local heterogenous effects that cannot be investigated using ordinary 2SLS.

     

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    hdl: 10419/224538
    Schriftenreihe: Jahrestagung 2020 / Verein für Socialpolitik ; 33
    Discussion paper series / IZA ; no. 13613
    Schlagworte: machine learning; generalized random forests; fertility,instrumental variable estimation
    Umfang: 1 Online-Ressource (circa 25 Seiten), Illustrationen
  11. How people pay each other
    data, theory, and calibrations
    Erschienen: [2021]
    Verlag:  Federal Reserve Bank of Atlanta, Atlanta, GA

    Using a representative sample of the U.S. adult population, we analyze which payment methods consumers use to pay other consumers (p2p) and how these choices depend on transaction and demographic characteristics. We additionally construct a random... mehr

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    Using a representative sample of the U.S. adult population, we analyze which payment methods consumers use to pay other consumers (p2p) and how these choices depend on transaction and demographic characteristics. We additionally construct a random matching model of consumers with diverse preferences over the use of different payment methods for p2p payments. The random matching model is calibrated to the share of p2p payments made with cash, paper check, and electronic technologies observed from 2015 to 2019. We find about two thirds of consumers have a first p2p payment preference of cash. The remaining one third rank checks first. Approximately 93 percent of consumers rank electronic technologies second. Our empirical analysis finds that the most significant factors in determining the payment method used are the transaction value and the age and education of the payer.

     

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    hdl: 10419/244314
    Schriftenreihe: Working paper series / Federal Reserve Bank of Atlanta ; 2021, 11 (February 2021)
    Schlagworte: consumer payment choice; person-to-person payments; electronic payments; mixed logit; machine learning; random matching
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  12. Measuring national happiness with music
    Erschienen: [2021]
    Verlag:  The University of Warwick, Centre for Competitive Advantage in the Global Economy, Department of Economics, Coventry, United Kingdom

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    Schriftenreihe: Working paper series / Centre for Competitive Advantage in the Global Economy ; no. 537 (January 2021)
    Schlagworte: subjective wellbeing; life satisfaction; national happiness; music informationretrieval; machine learning
    Umfang: 1 Online-Ressource (circa 14 Seiten), Illustrationen
  13. Won't get fooled again
    a supervised machine learning approach for screening gasoline cartels
    Erschienen: January 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we... mehr

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    In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we evaluate how the standard deviation, coefficient of variation, skewness, and kurtosis can be useful features in identifying anti-competitive market behavior. We complement our discussion with the so-called confusion matrix and discuss the trade-offs related to false-positive and false-negative predictions. Our results show that in some cases, false-negative outcomes critically increase when the main objective is to minimize false-positive predictions. We offer a discussion regarding the pros and cons of our approach for antitrust authorities aiming at detecting and avoiding gasoline cartels.

     

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    hdl: 10419/232432
    Schriftenreihe: CESifo working paper ; no. 8835 (2021)
    Schlagworte: cartel screens; price dynamics; fuel retail market; machine learning
    Umfang: 1 Online-Ressource (circa 51 Seiten), Illustrationen
  14. The value added of machine learning to causal inference
    evidence from revisited studies
    Erschienen: [2021]
    Verlag:  Tinbergen Institute, Amsterdam, The Netherlands

    A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these... mehr

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    A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods and identify several advantages of using these techniques. We show that these advantages and their implications are empirically relevant and that the use of these methods can improve the credibility of causal analysis.

     

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    hdl: 10419/229707
    Schriftenreihe: Array ; TI 2021, 001
    Schlagworte: machine learning; causal inference; average treatment effects; heterogeneous treatment effects
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  15. Using machine learning for measuring democracy
    an update
    Erschienen: February 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are... mehr

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    We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we largely expand the data coverage by providing democracy indicators for 186 countries in the period from 1919 to 2019.

     

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    hdl: 10419/235273
    Schriftenreihe: CESifo working paper ; no. 8903 (2021)
    Schlagworte: data aggregation; democracy indicators; machine learning; measurement issues; regime classifications; support vector machines
    Umfang: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  16. Wage expectation, information and the decision to become a nurse
    Autor*in: Kugler, Philipp
    Erschienen: Januar 2021
    Verlag:  Institut für Angewandte Wirtschaftsforschung e.V., Tübingen, Germany

    In light of skilled-labor shortage in nursing, the effect of a change in the wage of nurses on their labor supply is intensely discussed in recent literature. However, most results show a wage elasticity close to zero. Using extensive data of former... mehr

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    In light of skilled-labor shortage in nursing, the effect of a change in the wage of nurses on their labor supply is intensely discussed in recent literature. However, most results show a wage elasticity close to zero. Using extensive data of former German 9th graders, I analyze the role of the expected wage as an incentive to become a nurse. To estimate a causal effect, I select controls and their functional form using post-double-selection, which is a data driven selection method based on regression shrinkage via the lasso. Contrary to common perceptions, the expected wage plays a positive and statistically significant role in the decision to become a nurse. Further, understating a nurse's wage decreases the probability of becoming one. Concerning omitted variable bias, I assess the sensitivity of the results using a novel approach. It evaluates the minimum strength that unobserved confounders would need to change the conclusion. The sensitivity analysis shows that potential unobserved confounders would have to be very strong to overrule the conclusions. The empirical results lead to two important policy implications. First, increasing the wage may help to overcome the shortage observed in many countries. Second, providing information on the (relative) wage may be a successful strategy to attract more individuals into this profession.

     

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    hdl: 10419/249058
    Schriftenreihe: IAW discussion papers ; no. 135 (January 2021)
    Schlagworte: health professional; expected wage; wage information; machine learning; sensitivity analysis
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  17. The gender pay gap revisited with big data
    do methodological choices matter?
    Erschienen: February 2021
    Verlag:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in... mehr

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    The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included.

     

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    hdl: 10419/235282
    Schriftenreihe: CESifo working paper ; no. 8912 (2021)
    Schlagworte: gender inequality; gender pay gap; common support; model specification; matching estimator; machine learning
    Umfang: 1 Online-Ressource (circa 42 Seiten), Illustrationen
  18. Misogynistic and xenophobic hate language online
    a matter of anonymity
    Erschienen: [2020]
    Verlag:  Swedish Institute for Social Research (SOFI), Stockholm University, [Stockholm]

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    Schriftenreihe: Working paper / Swedish Institute for Social Research ; 2020, 7
    Schlagworte: online hate; anonymity; discussion forum; machine learning; big data
    Umfang: 1 Online-Ressource (circa 77 Seiten), Illustrationen
  19. The anatomy of out-of-sample forecasting accuracy
    Erschienen: [2022]
    Verlag:  Federal Reserve Bank of Atlanta, Atlanta, GA

    We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric... mehr

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    We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics, iShapley-VI and oShapley-VI, measure the importance of individual predictors in fitted models for explaining the in-sample and out-of-sample predicted target values, respectively. The third metric is the performance-based Shapley value (PBSV), our main methodological contribution. PBSV measures the contributions of individual predictors in fitted models to the out-of-sample loss and thereby anatomizes out-of-sample forecasting accuracy. In an empirical application forecasting US inflation, we find important discrepancies between individual predictor relevance according to the in-sample iShapley-VI and out-ofsample PBSV. We use simulations to analyze potential sources of the discrepancies, including overfitting, structural breaks, and evolving predictor volatilities.

     

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    hdl: 10419/270459
    Schriftenreihe: Working paper series / Federal Reserve Bank of Atlanta ; 2022, 16 (November 2022)
    Schlagworte: variable importance; out-of-sample performance; Shapley value; loss function; machine learning; inflation
    Umfang: 1 Online-Ressource (circa 54 Seiten), Illustrationen
  20. AI, skill, and productivity
    the case of taxi drivers
    Erschienen: October 2022
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    We examine the impact of Articial Intelligence (AI) on productivity in the context of taxi drivers. The AI we study assists drivers with finding customers by suggesting routes along which the demand is predicted to be high. We find that AI improves... mehr

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    We examine the impact of Articial Intelligence (AI) on productivity in the context of taxi drivers. The AI we study assists drivers with finding customers by suggesting routes along which the demand is predicted to be high. We find that AI improves drivers' productivity by shortening the cruising time, and such gain is accrued only to low-skilled drivers, narrowing the productivity gap between high- and low-skilled drivers by 14%. The result indicates that AI's impact on human labor is more nuanced and complex than a job displacement story, which was the primary focus of existing studies.

     

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    Weitere Identifier:
    hdl: 10419/267414
    Schriftenreihe: Discussion paper series / IZA ; no. 15677
    Schlagworte: artificial intelligence; skill; productivity; taxi-drivers; prediction; demand forecasting; machine learning
    Umfang: 1 Online-Ressource (circa 46 Seiten), Illustrationen
  21. Creating data from unstructured text with Context Rule Assisted Machine Learning (CRAML)
    Erschienen: 2022
    Verlag:  Global Labor Organization (GLO), Essen

    Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method... mehr

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    Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected "no poach" clauses in franchise documents.

     

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    Sprache: Englisch
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    hdl: 10419/267553
    Schriftenreihe: GLO discussion paper ; no. 1214
    Schlagworte: machine learning; natural language processing; text classification; big data
    Umfang: 1 Online-Ressource (circa 56 Seiten), Illustrationen
  22. What drives the relationship between digitalization and industrial energy demand?
    exploring firm-level heterogeneity
    Erschienen: [2022]
    Verlag:  ZEW - Leibniz Centre for European Economic Research, Mannheim, Germany

    The ongoing digital transformation has raised hopes for ICT-based climate protection within manufacturing industries, such as dematerialized products and energy efficiency gains. However, ICT also consume energy as well as resources, and detrimental... mehr

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    The ongoing digital transformation has raised hopes for ICT-based climate protection within manufacturing industries, such as dematerialized products and energy efficiency gains. However, ICT also consume energy as well as resources, and detrimental effects on the environment are increasingly gaining attention. Accordingly, it is unclear whether trade-offs or synergies between the use of digital technologies and energy savings exist. Our analysis sheds light on the most important drivers of the relationship between ICT and energy use in manufacturing. We apply flexible tree-based machine learning to a German administrative panel data set including more than 25,000 firms. The results indicate firm-level heterogeneity, but suggest that digital technologies relate more frequently to an increase in energy use. Multiple characteristics, such as energy prices and firms’ energy mix, explain differences in the effect.

     

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    hdl: 10419/266649
    Schriftenreihe: Discussion paper / ZEW ; no. 22, 059 (11/2022)
    Schlagworte: digital technologies; energy use; manufacturing; machine learning
    Umfang: 1 Online-Ressource (54 Seiten), Illustrationen
  23. Suptech in insurance supervision
    Erschienen: December 2022
    Verlag:  Bank for International Settlements, Financial Stability Institute, [Basel]

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    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9789292596194
    Schriftenreihe: FSI insights on policy implementation ; no 47
    Schlagworte: suptech; insurance; prudential supervision; conduct supervision; data analytics; innovation; AI; artificial intelligence; ML; machine learning; NLP; natural language processing
    Umfang: 1 Online-Ressource (circa 20 Seiten), Illustrationen
  24. Previsão de inflação
    análise preliminar de desempenho de técnicas de machine learning
    Erschienen: novembro de 2022
    Verlag:  Instituto de Pesquisa Econômica Aplicada, Rio de Janeiro

    In this Discussion Paper, we test forecasting models for inflation and economic activity with macroeconomic data and economic surveys between January 2002 and October 2019 on a monthly basis. Due to the high dimension nature of the set of explanatory... mehr

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    In this Discussion Paper, we test forecasting models for inflation and economic activity with macroeconomic data and economic surveys between January 2002 and October 2019 on a monthly basis. Due to the high dimension nature of the set of explanatory variables, we use machine learning (ML) models that offer different ways to deal with large datasets and we compare with benchmark models. We find that ML methods substantially improve inflation forecasts for shorter horizons (one and three months). While Least Absolute Shrinkage and Selection Operator (Lasso) is the model that best performs for the one-month horizon, a combination of ML models performs better for the three months horizon. However, for longer-term horizons (six and twelve months), individual ML methods and economic surveys do not perform well, despite the fact that a combination of ML models are better than benchmark models. Concerning GDP forecasts, the reverse is true. ML methods do not perform well for the one month horizon, but combinations of ML methods (three and twelve month) and complete subset regression (CSR) (six month) overcame traditional models.

     

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    Sprache: Portugiesisch
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    hdl: 10419/284870
    Schriftenreihe: Texto para discussão / Ipea ; 2814
    Schlagworte: forecast; econometrics; macroeconomics; machine learning
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  25. Acquisition of costly information in data-driven decision making
    Autor*in: Janasek, Lukas
    Erschienen: [2022]
    Verlag:  Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Prague

    This paper formulates and solves an economic decision problem of the acquisition of costly information in data-driven decision making. The paper assumes an agent predicting a random variable utilizing several costly explanatory variables. Prior to... mehr

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    This paper formulates and solves an economic decision problem of the acquisition of costly information in data-driven decision making. The paper assumes an agent predicting a random variable utilizing several costly explanatory variables. Prior to the decision making, the agent learns about the relationship between the random variables utilizing its past realizations. During the decision making, the agent decides what costly variables to acquire and predicts using the acquired variables. The agent's utility consists of the correctness of the prediction and the costs of the acquired variables. To solve the decision problem, we split the decision process into two parts: acquisition of variables and prediction using the acquired variables. For the prediction, we propose an approach for training a single predictive model accepting any combination of acquired variables. For the acquisition, we propose two methods using supervised machine learning models: a backward estimation of the expected utility of each variable and a greedy acquisition of variables based on a myopic estimate of the expected utility. We evaluate the methods on two medical datasets. The results show that the methods acquire the costly variables efficiently.

     

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    hdl: 10419/265196
    Schriftenreihe: IES working paper ; 2022, 10
    Schlagworte: costly information; data-driven decision-making; machine learning
    Umfang: 1 Online-Ressource (circa 31 Seiten), Illustrationen