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  1. Transnational machine learning with screens for flagging bid-rigging cartels
    Autor*in: Huber, Martin; Imhof, David; Ishii, Rieko
    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
  2. Predicting fiscal crises
    a machine learning approach
    Autor*in: Hellwig, Klaus-Peter
    Erschienen: May 2021
    Verlag:  International Monetary Fund, [Washington, D.C.]

    In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb.... mehr

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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Potsdamer Straße
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    Universitätsbibliothek Braunschweig
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    In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors

     
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    ISBN: 9781513573588
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    Schriftenreihe: IMF working paper ; WP/21, 150
    Schlagworte: Early warning systems; sovereign default; random forest; Foreign Exchange; Informal Economy; Underground Econom
    Umfang: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  3. Exploration of machine learning algorithms for maritime risk applications
    Autor*in: Knapp, Sabine; Velden, Michel van de
    Erschienen: [2021]
    Verlag:  [Econometric Institute, Erasmus School of Economics], [Rotterdam]

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    Weitere Identifier:
    hdl: 1765/137081
    Schriftenreihe: Econometric Institute report ; 2021, 3
    Schlagworte: ship specific risk; safety quality; reducing false negative events; risk exposure estimation; machine learning; case weighting; subsampling; random forest; sampling; evaluation metrics; top decile lift; variable importance; machine learning
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen
  4. A machine learning approach to volatility forecasting
    Autor*in: Christensen, Kim; Siggaard, Mathias Voldum; Veliyev, Bezirgen
    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
  5. Incentive effects of R&D tax incentives
    a meta-analysis focusing on R&D tax policy designs
    Autor*in: Pöschel, Carla
    Erschienen: [2020]
    Verlag:  Arbeitskreis Quantitative Steuerlehre, Berlin

    Despite the growing literature on the effectiveness of research and development (R&D) tax incentives, little is known about the differing design aspects of the underlying tax policies. In this paper, I apply meta-regression analysis (MRA) to separate... mehr

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    Despite the growing literature on the effectiveness of research and development (R&D) tax incentives, little is known about the differing design aspects of the underlying tax policies. In this paper, I apply meta-regression analysis (MRA) to separate the distinct provisions through which various tax schemes affect firms' R&D expenditures. Using 192 estimates from 19 studies exploiting the direct approach, the results indicate, on average, greater input additionality effects of hybrid regimes in comparison to volume-based and incremental ones. MetaForest, a novel machine learning algorithm, confirms these results: the moderator for hybrid schemes is the most important variable in explaining the heterogeneity among estimates. Unlike previous MRA, I find only weak evidence for publication bias in this stream of literature. Overall, the relation between tax incentives and R&D expenditures is positive, on average, but the strength varies with methodological variations across studies.

     
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    hdl: 10419/230675
    Auflage/Ausgabe: Revised and renamed August 2020
    Schriftenreihe: Arqus discussion paper ; no. 243 (August 2019)
    Schlagworte: R&D; tax incentives; additionality effects; direct approach; meta-regression analysis; random forest
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  6. Bottom incomes and the measurement of poverty and inequality
    Autor*in: Hlásny, Vladimír; Ceriani, Lidia; Verme, Paolo
    Erschienen: May 2020
    Verlag:  ECINEQ, Society for the Study of Economic Inequality, [Verona]

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    Schriftenreihe: Working paper series / ECINEQ, Society for the Study of Economic Inequality ; 535 (2020)
    Schlagworte: Bottom incomes; income inequality; poverty; self-employment; Mediterranean; Middle East; Pareto; random forest
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  7. Forecasting realized volatility using machine learning and mixed-frequency data (the case of the Russian stock market)
    Autor*in: Pyrlik, Vladimir; Elizarov, Pavel; Leonova, Aleksandra
    Erschienen: Noember 2021
    Verlag:  Charles University, Center for Economic Research and Graduate Education, Prague

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    ISBN: 9788073435202; 9788073446154
    Schriftenreihe: Working paper series / CERGE-EI ; 713
    Schlagworte: heterogeneous autoregressive model; machine learning; lasso; gradientboosting; random forest; long short-term memory; realized volatility; Russian stockmarket; mixed-frequency data
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  8. The hard problem of prediction for conflict prevention
    Autor*in: Mueller, Hannes; Rauh, Christopher
    Erschienen: December 2020
    Verlag:  GSE, Graduate School of Economics, Barcelona

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    Schriftenreihe: Barcelona GSE working paper series ; no 1244
    Schlagworte: Prognoseverfahren; Politischer Konflikt; Prävention; Künstliche Intelligenz; Kostenfunktion; armed conflict; forecasting; machine learning; newspaper text; random forest; topic models
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  9. Predicting football outcomes from Spanish league using machine learning models
    Autor*in: Lewandowski, Michał; Chlebus, Marcin
    Erschienen: 2021
    Verlag:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    Schriftenreihe: Working papers / University of Warsaw, Faculty of Economic Sciences ; no. 2021, 22 = 370
    Schlagworte: predicting football outcomes; machine learning; betting; adaboost; random forest; xgboost; catboost; ranked probability score; auc; permutation feature importance
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  10. Ensemble Learning for Portfolio Valuation and Risk Management
    Autor*in: Boudabsa, Lotfi; Filipović, Damir
    Erschienen: [2022]
    Verlag:  Swiss Finance Institute, Geneva

    We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is... mehr

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    We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. The method can also be applied to Bermudan style options. Numerical experiments show good results in moderate dimension problems

     
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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 22, 30
    Schlagworte: dynamic portfolio valuation; ensemble learning; gradient boosting; random forest; regression trees; risk management; Bermudan options
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  11. Distributional aspects of microcredit expansions
    Autor*in: Christiansen, Tobias G.; Weeks, Melvyn
    Erschienen: [2020]
    Verlag:  University of Cambridge, Faculty of Economics, Cambridge

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    Schriftenreihe: Cambridge working paper in economics ; 20100
    Schlagworte: Machine learning methods; microcredit; development policy; treatment effects; random forest; elastic net
    Umfang: 1 Online-Ressource (circa 39 Seiten)
  12. Mean convergence, combinatorics, and grade-point averages
    Autor*in: Waddell, Glen R.; McDonough, Robert
    Erschienen: July 2022
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    While comparing students across large differences in GPA follows one's intuition that higher GPAs correlate positively with higher-performing students, this need not be the case locally. Grade-point averaging is fundamentally a combinatorics problem,... mehr

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    While comparing students across large differences in GPA follows one's intuition that higher GPAs correlate positively with higher-performing students, this need not be the case locally. Grade-point averaging is fundamentally a combinatorics problem, and thereby challenges inference based on local comparisons - this is especially true when students have experienced only small numbers of classes. While the effect of combinatorics diminishes in larger numbers of classes, mean convergence then has us jeopardize local comparability as GPA better delineates students of different ability. Given these two characteristics in decoding GPA, we discuss the advantages of machine-learning approaches to identifying treatment in educational settings.

     
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    Schriftenreihe: Discussion paper series / IZA ; no. 15414
    Schlagworte: GPA; grades; program evaluation; random forest; regression discontinuity
    Umfang: 1 Online-Ressource (circa 51 Seiten), Illustrationen
  13. Targeted bidders in government tenders
    Autor*in: Cappelletti, Matilde; Giuffrida, Leonardo M.
    Erschienen: [2022]
    Verlag:  ZEW - Leibniz Centre for European Economic Research, Mannheim, Germany

    A set-aside restricts participation in procurement contests to targeted firms. Despite being widely used, its effects on actual competition and contract outcomes are ambiguous. We pool a decade of US federal procurement data to shed light on this... mehr

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    A set-aside restricts participation in procurement contests to targeted firms. Despite being widely used, its effects on actual competition and contract outcomes are ambiguous. We pool a decade of US federal procurement data to shed light on this empirical question using a two-stage approach. To circumvent the lack of exogenous variation in our data, as a first step we draw on random forest techniques to calculate the likelihood of a tender being set aside. We then estimate the effect of restricted tenders on pre- and postaward outcomes using an inverse probability weighting regression adjustment. Set-asides prompt more firms to bid - that is, the increase in targeted bidders more than offsets the loss of untargeted. During the execution phase, set-aside contracts incur higher cost overruns and delays. The more restrictive the setaside, the stronger these effects. In a subset of our data we leverage an expected spike in set-aside spending and we find no evidence of better performance by winners over a ten-year period.

     
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    hdl: 10419/261382
    Schriftenreihe: Discussion paper / ZEW ; no. 22, 030 (07/2022)
    Schlagworte: small businesses; set-aside; competition; procurement; public contracts; random forest; firm dynamics
    Umfang: 1 Online-Ressource (63 Seiten), Illustrationen
  14. Determinants of regional raw milk prices in Russia
    paper prepared for presentation at the 61th annual conference of the GEWISOLA
    Autor*in: Kresovaa, Svetlana; Hess, Sebastian
    Erschienen: 2021
    Verlag:  GEWISOLA, [Braunschweig]

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    Schlagworte: milk price; Russia; machine learning; random forest
    Umfang: 1 Online-Ressource (circa 14 Seiten), Illustrationen
  15. Predicting voter ideology using machine learning
    Autor*in: Mellacher, Patrick; Lechner, Gernot
    Erschienen: [2023]
    Verlag:  Graz Schumpeter Centre, [Graz]

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    Schriftenreihe: GSC discussion paper series ; paper no. 29
    Schlagworte: machine learning; random forest; voter ideology; political economy; spatial voting
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  16. The application of machine learning algorithms for spatial analysis
    predicting of real estate prices in Warsaw
    Autor*in: Siwicki, Dawid
    Erschienen: 2021
    Verlag:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    Schriftenreihe: Working papers / Faculty of Economic Sciences, University of Warsaw ; no. 2021, 5 = 353
    Schlagworte: spatial analysis; machine learning; housing market; random forest; gradient boosting
    Umfang: 1 Online-Ressource (circa 27 Seiten), Illustrationen
  17. Using past violence and current news to predict changes in violence
    Autor*in: Mueller, Hannes; Rauh, Christopher
    Erschienen: [2022]
    Verlag:  University of Cambridge, Faculty of Economics, Cambridge

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    Schriftenreihe: Cambridge working paper in economics ; 2220
    Janeway Institute working paper series ; 2022, 09
    Schlagworte: Conflict; prediction; machine learning; LDA; topic model; battle deaths; ViEWS prediction competition; random forest
    Umfang: 1 Online-Ressource (circa 16 Seiten), Illustrationen
  18. Forecasting realized volatility in turbulent times using temporal fusion transformers
    Autor*in: Frank, Johannes
    Erschienen: [2023]
    Verlag:  Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, [Nürnberg]

    This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well... mehr

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    This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well as GARCH models. The models are trained on weekly and monthly data based on three different feature sets using varying training approaches including pooling methods. I find that temporal fusion transformers show very good results in predicting financial volatility and outperform long short-term memory networks and random forests when using pooling methods. The use of sectoral pooling substantially improves the predictive performance of all machine learning approaches used. The results are robust to different ways of training the models.

     
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    hdl: 10419/268951
    Schriftenreihe: FAU discussion papers in economics ; no. 2023, 03
    Schlagworte: Realized volatility; temporal fusion transformer; long short-term memory network; random forest
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  19. A survey on AI-based scheduling models, optimization and prediction for hydropower generation
    variants, challenges, and future directions
    Autor*in: Villeneuve, Yoan; Séguin, Sara; Chehri, Abdellah
    Erschienen: [2022]
    Verlag:  GERAD, HÉC Montréal, Montréal (Québec), Canada

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    Schriftenreihe: Les cahiers du GERAD ; G-2022, 52 (Decembre 2022)
    Schlagworte: Hydropower; hydropower scheduling; machine learning; optimization; stochastic programming; linear regression; random forest; reinforcement learning; Deep Neural Networks
    Umfang: 1 Online-Ressource (circa 28 Seiten), Illustrationen
  20. Predicting fiscal crises
    a machine learning approach
    Autor*in: Hellwig, Klaus-Peter
    Erschienen: May 2021
    Verlag:  International Monetary Fund, [Washington, D.C.]

    In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb.... mehr

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    In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors

     
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    ISBN: 9781513573588
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    Schriftenreihe: IMF working paper ; WP/21, 150
    Schlagworte: Early warning systems; sovereign default; random forest; Foreign Exchange; Informal Economy; Underground Econom
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  21. Feature Importance for Human Epithelial (HEp-2) Cell Image Classification
    Autor*in: Vibha Gupta; Arnav Bhavsar
    Erschienen: 2018
    Verlag:  MDPI AG

    Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based... mehr

     

    Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

     
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    Übergeordneter Titel: Journal of Imaging, Vol 4, Iss 3, p 46 (2018)
    Schlagworte: feature selection; filter methods; hybridization; random forest; class-specific features; Photography; Computer applications to medicine. Medical informatics; Electronic computers. Computer science
  22. Strategic management in public procurement
    the role of dynamic capabilities in equity and efficiency
    Autor*in: Cappelletti, Matilde; Giuffrida, Leonardo M.; Heaton, Sohvi; Siegel, Donald S.
    Erschienen: [2023]
    Verlag:  ZEW - Leibniz Centre for European Economic Research, Mannheim, Germany

    A key issue in strategic management in the public sector is how government creates economic and social value through procurement. Unfortunately, most procurement studies are based on contract theories, which fail to incorporate the growing role of... mehr

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    A key issue in strategic management in the public sector is how government creates economic and social value through procurement. Unfortunately, most procurement studies are based on contract theories, which fail to incorporate the growing role of strategic management in performance. We fill this gap by analyzing longitudinal data on contracting to assess the equity and efficiency effects of a form of affirmative action used by governments: set-aside programs. Employing a machine learning-augmented propensity score weighting approach, we find that set-aside contracts are negatively associated with contract performance. These effects are attenuated by an agency’s dynamic capabilities and the extent to which the agency uses more competitive procedures. Our findings illustrate how the dynamic capabilities of a federal agency can simultaneously enhance equity and efficiency.

     
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    Weitere Identifier:
    hdl: 10419/277729
    Schriftenreihe: Discussion paper / ZEW ; no. 23, 035 (09/2023)
    Schlagworte: Dynamic capabilities; resource-based view; public procurement; machine learning; random forest
    Umfang: 1 Online-Ressource (52 Seiten), Illustrationen
  23. Predicting re-employment
    machine learning versus assessments by unemployed workers and by their caseworkers
    Autor*in: Berg, Gerard J. van den; Kunaschk, Max; Lang, Julia; Stephan, Gesine; Uhlendorff, Arne
    Erschienen: September 2023
    Verlag:  IZA - Institute of Labor Economics, Bonn, Germany

    Predictions of whether newly unemployed individuals will become long-term unemployed are important for the planning and policy mix of unemployment insurance agencies. We analyze unique data on three sources of information on the probability of... mehr

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    Predictions of whether newly unemployed individuals will become long-term unemployed are important for the planning and policy mix of unemployment insurance agencies. We analyze unique data on three sources of information on the probability of re-employment within 6 months (RE6), for the same individuals sampled from the inflow into unemployment. First, they were asked for their perceived probability of RE6. Second, their caseworkers revealed whether they expected RE6. Third, random-forest machine learning methods are trained on administrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider whether combinations improve this performance. We show that self-reported and caseworker assessments sometimes contain information not captured by the machine learning algorithm.

     
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    hdl: 10419/279124
    Schriftenreihe: Discussion paper series / IZA ; no. 16426
    Schlagworte: unemployment; expectations; prediction; random forest; unemployment insurance; information
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  24. The populist voter
    a machine learning approach for the individual characteristics
    Autor*in: Arin, Kerim Peren; Polyzos, Efstathios; Thum, Marcel
    Erschienen: May 2023
    Verlag:  CESifo, Munich, Germany

    Populist parties recently have shaken Western democracies, yet there is no consensus regarding the characteristics of populist voters. By using large-scale surveys from four European countries (France, Germany, Spain, and the U.K.), we investigate... mehr

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    Populist parties recently have shaken Western democracies, yet there is no consensus regarding the characteristics of populist voters. By using large-scale surveys from four European countries (France, Germany, Spain, and the U.K.), we investigate individual determinants of populist voting. Our methodological approach controls for model uncertainty by considering the responses to 100 questions that span social, economic, political, environmental, and psychological dimensions. We also include individual misperceptions across several domains. Our results show that left-wing populist voters are not religious, have lower misperceptions regarding foreign-national prisoners, distrust the police, are open to immigrants from poorer countries, and oppose dismantling the welfare state. The right-wing populist voters oppose incoming, racially diverse immigrants, distrust national and international institutions, and have high misperceptions regarding immigrant crimes and the share of social benefits in the GDP. Contrary to the previous literature, attitudes toward globalization, personality traits, labor-market status, and social media use are not consensus variables for either group.

     
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    Weitere Identifier:
    hdl: 10419/279221
    Schriftenreihe: CESifo working papers ; 10472 (2023)
    Schlagworte: populism; random forest; Bayesian model averaging
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  25. Nowcasting domestic liquidity in the Philippines using machine learning algorithms
    Autor*in: Reyes, Juan Rufino M.
    Erschienen: March 2022
    Verlag:  Bangko Sentral ng Pilipinas, [Malate Manila, Philippines]

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    Schriftenreihe: BSP working paper series ; no. 2022, 04
    Schlagworte: nowcasting; fomestic liquidity; time series; ARIMA; dynamic factor model; machine learning; ridge regression; LASSO; elastic net; random forest; gradient boosted trees
    Umfang: 1 Online-Ressource (circa 73 Seiten), Illustrationen