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  1. "Seeing" the future
    improving macroeconomic forecasts with spatial data and recurrent convolutional neural networks
    Erschienen: March 9, 2023
    Verlag:  CAEPR, Center for Applied Economics and Policy Research, [Bloomington, IN]

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    Schriftenreihe: CAEPR working paper ; #2023, 003
    Schlagworte: Macroeconomic Forecasting; Machine Learning; Deep Learning; Computer Vision; Economic Geography
    Umfang: 1 Online-Ressource (circa 23 Seiten), Illustrationen
  2. A deep learning approach to estimate forward default intensities
    Erschienen: July 21, 2020
    Verlag:  Swiss Finance Institute, Geneva

    This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default... mehr

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    This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved

     

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    Auflage/Ausgabe: Preliminary draft
    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 20, 79
    Swiss Finance Institute Research Paper ; No. 20-79
    Schlagworte: Bankruptcy; Credit Risk; Default; Machine Learning; Neural Networks; Doubly Stochastic; Forward Poisson Intensities
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  3. A machine learning projection method for macro-finance models
    Erschienen: [2022]
    Verlag:  Federal Reserve Bank of Chicago, [Chicago, Illinois]

    This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic... mehr

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    This paper develops a simulation-based solution method to solve large state space macrofinance models using machine learning. We use a neural network (NN) to approximate the expectations in the optimality conditions in the spirit of the stochastic parameterized expectations algorithm (PEA). Because our method can process the entire information set at once, it is scalable and can handle models with large and multicollinear state spaces. We demonstrate the computational gains by extending the optimal government debt management problem studied by Faraglia et al. (2019) from two to three maturities. We find that the optimal policy prescribes an active role for the newly added medium-term maturity, enabling the planner to raise financial income without increasing its total borrowing in response to expenditure shocks. Through this mechanism the government effectively subsidizes the private sector in recessions, resulting in a welfare gain of 2.38% when the number of available maturities increases from two to three.

     

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    hdl: 10419/264333
    Schriftenreihe: [Working paper] / Federal Reserve Bank of Chicago ; WP 2022, 19 (November 18, 2021)
    Schlagworte: Machine Learning; Incomplete Markets; Projection Methods; Optimal Fiscal Policy; Maturity Management
    Umfang: 1 Online-Ressource (circa 50 Seiten), Illustrationen
  4. Addressing unemployment rate forecast errors in relation to the business cycle
    Autor*in: Scheer, Bas
    Erschienen: March 2022
    Verlag:  CPB Netherlands Bureau for Economic Policy Analysis, [Den Haag]

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    Schriftenreihe: CPB discussion paper
    Schlagworte: Unemployment rate; Forecast errors; Machine Learning
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  5. Algorithm, Human, or the Centaur
    How to Enhance Clinical Care?

    There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making... mehr

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    There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making is not well understood. Our work attempts to address this problem, investigating the effect of algorithmic predictions on human expert judgment. Leveraging an online survey of medical providers and data from a leading U.S. hospital, we develop a ML algorithm and compare its performance with that of medical experts in the task of predicting 30-day readmissions after solid-organ transplantation. We find that our algorithm is not only more accurate in predicting clinical risk but can also positively influence human judgment. However, its potential impact is mediated by the users’ degree of algorithm aversion and trust. We show that, while our ML algorithm establishes non-linear associations between patient characteristics and the outcome of interest, human experts mostly attribute risk in a linear fashion. To capture potential synergies between human experts and the algorithm, we propose a human-algorithm “centaur” model. We show that it is able to outperform human experts and the best ML algorithm by systematically enhancing algorithmic performance with human-based intuition. Our results suggest that implementing the centaur model could reduce the average patient readmission rate by 26.4%, yielding up to a $770k reduction in annual expenditure at our partner hospital and up to $67 million savings in overall U.S. healthcare expenditures

     

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    Schriftenreihe: HKS Working Paper ; No. RWP22-027
    Schlagworte: Machine Learning; Transplantation; Health care; Hospital Readmission; Human-Algorithm Interactions
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (37 p)
    Bemerkung(en):

    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 14, 2022 erstellt

  6. Another piece of the puzzle
    adding SWIFT data on documentary collections to the short-term forecast of world trade
    Erschienen: Dec 2021
    Verlag:  International Monetary Fund, [Washington, D.C.]

    This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have... mehr

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    This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies. The informational content from documentary collections helps improve the forecast of world trade, while a horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic

     

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    Quelle: Staatsbibliothek zu Berlin
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781616357634
    Weitere Identifier:
    Schriftenreihe: Working paper / International Monetary Fund ; WP/21, 293
    Schlagworte: SWIFT; trade forecast; machine learning; Machine Learning; Macroeconomic Aspects of International Trade and Finance; SWIFT; Trade Forecast; Trade
    Umfang: 1 Online-Ressource (circa 63 Seiten), Illustrationen
  7. Another piece of the puzzle
    adding SWIFT data on documentary collections to the short-term forecast of world trade
    Erschienen: Dec 2021
    Verlag:  International Monetary Fund, [Washington, D.C.]

    This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have... mehr

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    This paper extends earlier research by adding SWIFT data on documentary collections to the short-term forecast of international trade. While SWIFT documentary collections accounted for just over one percent of world trade financing in 2020, they have strong explanatory power to forecast world trade and national trade in selected economies. The informational content from documentary collections helps improve the forecast of world trade, while a horse race with machine learning algorithms shows significant non-linearities between trade and its determinants during the Covid-19 pandemic

     

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    Quelle: Staatsbibliothek zu Berlin
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781616357634
    Weitere Identifier:
    Schriftenreihe: Working paper / International Monetary Fund ; WP/21, 293
    Schlagworte: SWIFT; trade forecast; machine learning; Machine Learning; Macroeconomic Aspects of International Trade and Finance; SWIFT; Trade Forecast; Trade
    Umfang: 1 Online-Ressource (circa 63 Seiten), Illustrationen
  8. Applications in high-dimensional econometrics
    Erschienen: 2021
    Verlag:  Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, Hamburg

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    Sprache: Englisch
    Medientyp: Dissertation
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    Schlagworte: Machine Learning; Double Machine Learning; Lasso; Simultaneous Inference; Gender Wage Gap; Post Double-Selection
    Umfang: 1 Online-Ressource (circa 245 Seiten), Illustrationen
    Bemerkung(en):

    Dissertation, Universität Hamburg, 2021

  9. Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning
    Erschienen: December 2020
    Verlag:  World Bank Group, Development Economics, Development Impact Evaluation Group, [Washington, DC, USA]

    With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed... mehr

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    With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed into a resource for urban planning and development. The hypothesis is tested by creating road traffic crash location data, which are scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over age five and young adults. The research project scraped 874,588 traffic-related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. The project geolocated 32,991 crash reports in Twitter for 2012-20 and clustered them into 22,872 unique crashes to produce one of the first crash maps for Nairobi. A motorcycle delivery service was dispatched in real-time to verify a subset of crashes, showing 92 percent accuracy. Using a spatial clustering algorithm, portions of the road network (less than 1 percent) were identified where 50 percent of the geolocated crashes occurred. Even with limitations in the representativeness of the data, the results can provide urban planners useful information to target road safety improvements where resources are limited

     

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    Quelle: Staatsbibliothek zu Berlin
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Policy research working paper ; 9488
    World Bank E-Library Archive
    Schlagworte: Big Data; Machine Learning; Road Safety; Urban Mobility; SDGs
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  10. Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning
    Erschienen: December 2020
    Verlag:  World Bank Group, Development Economics, Development Impact Evaluation Group, [Washington, DC, USA]

    With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed... mehr

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    With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. This project set out to test whether an openly available dataset (Twitter) could be transformed into a resource for urban planning and development. The hypothesis is tested by creating road traffic crash location data, which are scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over age five and young adults. The research project scraped 874,588 traffic-related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. The project geolocated 32,991 crash reports in Twitter for 2012-20 and clustered them into 22,872 unique crashes to produce one of the first crash maps for Nairobi. A motorcycle delivery service was dispatched in real-time to verify a subset of crashes, showing 92 percent accuracy. Using a spatial clustering algorithm, portions of the road network (less than 1 percent) were identified where 50 percent of the geolocated crashes occurred. Even with limitations in the representativeness of the data, the results can provide urban planners useful information to target road safety improvements where resources are limited

     

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    Quelle: Staatsbibliothek zu Berlin
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Policy research working paper ; 9488
    World Bank E-Library Archive
    Schlagworte: Big Data; Machine Learning; Road Safety; Urban Mobility; SDGs
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  11. Artificial intelligence in the field of economics
    Erschienen: [2021]
    Verlag:  CREMA, Zürich

    The history of AI in economics is long and winding, much the same as the evolving field of AI itself. Economists have engaged with AI since its beginnings, albeit in varying degrees and with changing focus across time and places. In this study, we... mehr

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    The history of AI in economics is long and winding, much the same as the evolving field of AI itself. Economists have engaged with AI since its beginnings, albeit in varying degrees and with changing focus across time and places. In this study, we have explored the diffusion of AI and different AI methods (e.g., machine learning, deep learning, neural networks, expert systems, knowledgebased systems) through and within economic subfields, taking a scientometrics approach. In particular, we centre our accompanying discussion of AI in economics around the problems of economic calculation and social planning as proposed by Hayek. To map the history of AI within and between economic subfields, we construct two datasets containing bibliometrics information of economics papers based on search query results from the Scopus database and the EconPapers (and IDEAs/RePEc) repository. We present descriptive results that map the use and discussion of AI in economics over time, place, and subfield. In doing so, we also characterise the authors and affiliations of those engaging with AI in economics. Additionally, we find positive correlations between quality of institutional affiliation and engagement with or focus on AI in economics and negative correlations between the Human Development Index and share of learning-based AI papers.

     

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    Sprache: Englisch
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    Format: Online
    Weitere Identifier:
    hdl: 10419/246011
    Schriftenreihe: Working paper / CREMA, Center for Research in Economics, Management and the Arts ; no. 2021, 28
    Schlagworte: Artificial Intelligence; Machine Learning; Economics; Scientometrics; Scienceof Science; Bibliometrics
    Umfang: 1 Online-Ressource (circa 29 Seiten), Illustrationen
  12. Artificial neural networks to solve dynamic programming problems
    a bias-corrected Monte Carlo operator
    Autor*in: Pascal, Julien
    Erschienen: March 2023
    Verlag:  Banque centrale du Luxembourg, Luxembourg

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    Schriftenreihe: Working paper / Banque centrale du Luxembourg ; no 172
    Schlagworte: Dynamic programming; Artificial Neural Network; Machine Learning; Monte Carlo
    Umfang: 1 Online-Ressource (circa 42 Seiten), Illustrationen
  13. Artificially intelligent marketplaces
    Erschienen: [2022]
    Verlag:  INSEAD, [Fontainebleau]

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    Schriftenreihe: Array ; 2022, 26
    Schlagworte: Marketplaces; Platforms; Artificial Intelligence; Bots; Back-Testing; Pricing; Nonfungible Tokens; Machine Learning
    Umfang: 1 Online-Ressource (circa 31 Seiten), Illustrationen
  14. Asset pricing with realistic crises dynamics
    Erschienen: 2020
    Verlag:  Swiss Finance Institute, Geneva

    What causes deep recessions and slow recovery? I revisit this question and develop a macro-finance model that quantitatively matches the salient empirical features of financial crises such as a large drop in the output, a high risk premium, reduced... mehr

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    What causes deep recessions and slow recovery? I revisit this question and develop a macro-finance model that quantitatively matches the salient empirical features of financial crises such as a large drop in the output, a high risk premium, reduced financial intermediation, and a long duration of economic distress. The model has leveraged intermediaries featuring stochastic productivity and a regime-dependent exit rate that governs the transition in and out of crises. A model without these two features suffers from a trade-off between the amplification and persistence of crises. I show that my model resolves this tension and generates realistic crisis dynamics

     

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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 20, 96
    Schlagworte: Financial Intermediation; Intermediary Asset Pricing; Machine Learning
    Umfang: 1 Online-Ressource (circa 71 Seiten), Illustrationen
  15. Automatic product classification in international trade
    machine learning and large language models
    Erschienen: July 2023
    Verlag:  Inter-American Development Bank, Integration and Trade Sector, [Washington, DC]

    Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we... mehr

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    Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.

     

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    Auflage/Ausgabe: This version: July 2023
    Schriftenreihe: IDB working paper series ; no IDB-WP-01494
    Schlagworte: Product Classification; Machine Learning; Large Language Models; Trade
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  16. Bayesian machine learning for financial modeling
    Erschienen: 2021
    Verlag:  Universitätsbibliothek Johann Christian Senckenberg, Frankfurt am Main

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    Beteiligt: Bertschinger, Nils (AkademischeR BetreuerIn)
    Sprache: Englisch
    Medientyp: Dissertation
    Format: Online
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    Schlagworte: Bayesian Statistics; Finance; Gaussian Processes; Machine Learning
    Umfang: 1 Online-Ressource (circa 127 Seiten), Illustrationen
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    Dissertation, Frankfurt am Main, Johann Wolfgang Goethe-Universität, 2021

  17. Beefing IT up for your investor?
    open sourcing and startup funding: evidence from Github
    Erschienen: [2021]
    Verlag:  Harvard Business School, [Boston, MA]

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    Schriftenreihe: Working paper / Harvard Business School ; 22, 001
    Schlagworte: Startups; Technology Strategy; GitHub; Machine Learning; Venture Capital
    Umfang: 1 Online-Ressource (circa 51 Seiten), Illustrationen
  18. Behavioral economics & machine learning
    expanding the field through a new lens
    Erschienen: 2021

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    RVK Klassifikation: QC 010
    Schlagworte: Behavioral Economics; Experimental Economics; Law and Economics; Empirical Law; Machine Learning; Natural Language Processing; Language and Behavior
    Umfang: 1 Online-Ressource (xi, 121, CXXVIII Seiten), Illustrationen, Diagramme
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    Literaturverzeichnis: Seite CXI-CXXIV)

    Dissertation, Universität zu Köln, 2021

  19. CATE meets ML
    conditional average treatment effect and machine learning
    Autor*in: Jacob, Daniel
    Erschienen: [2021]
    Verlag:  International Research Training Group 1792, Berlin

    For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with... mehr

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    For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory allows us to estimate not only the average but a personalized treatment effect - the conditional average treatment effect (CATE). In this tutorial, we give an overview of novel methods, explain them in detail, and apply them via Quantlets in real data applications. We study the effect that microcredit availability has on the amount of money borrowed and if the 401(k) pension plan eligibility has an impact on net financial assets, as two empirical examples. The presented toolbox of methods contains metalearners, like the Doubly-Robust, the R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized random forest. In both, the microcredit and the 401(k) example, we find a positive treatment effect for all observations but diverse evidence of treatment effect heterogeneity. An additional simulation study, where the true treatment effect is known, allows us to compare the different methods and to observe patterns and similarities.

     

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    hdl: 10419/233509
    Schriftenreihe: IRTG 1792 discussion paper ; 2021, 005
    Schlagworte: Causal Inference; CATE; Machine Learning; Tutorial
    Umfang: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  20. Can machine learning catch the COVID-19 recession?
    Erschienen: [2021]
    Verlag:  CIRANO, [Montréal]

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    Schriftenreihe: Cahier scientifique / CIRANO ; 2021s, 09
    Schlagworte: Machine Learning; Big Data; Forecasting; COVID-19
    Umfang: 1 Online-Ressource (circa 41 Seiten), Illustrationen
  21. Catching the drivers of inclusive growth in Sub-Saharan Africa
    an application of machine learning
    Erschienen: [2021]
    Verlag:  African Governance and Development Institute, [Yaoundé]

    A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum... mehr

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    A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum BIC) lasso, and the Adaptive lasso to study patterns in a dataset comprising 97 covariates of inclusive growth for 43 SSA countries. First, the regularization results show that only 13 variables are key for driving inclusive growth in SSA. Further, the results show that out of the 13, the poverty headcount (US$1.90) matters most. Second, the findings reveal that 'Minimum BIC lasso' is best for predicting inclusive growth in SSA. Policy recommendations are provided in line with the region's green agenda and the coming into force of the African Continental Free Trade Area.

     

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    hdl: 10419/244219
    Schriftenreihe: AGDI working paper ; WP/21, 044
    Schlagworte: Clean Fuel; Economic Growth; Machine Learning; Lasso; Sub-Saharan Africa; Regularization; Poverty
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  22. Central bank mandates and monetary policy stances
    through the lens of Federal Reserve speeches
    Erschienen: October 2022
    Verlag:  Sveriges Riksbank, Stockholm

    When does the Federal Reserve deviate from its dual mandate of pursuing the economic goals of maximum employment and price stability and what are the consequences? We assemble the most comprehensive collection of Federal Reserve speeches to-date and... mehr

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    When does the Federal Reserve deviate from its dual mandate of pursuing the economic goals of maximum employment and price stability and what are the consequences? We assemble the most comprehensive collection of Federal Reserve speeches to-date and apply state-ofthe-art natural language processing methods to extract a variety of textual features from each paragraph of each speech. We find that the periodic emergence of non-dual mandate related discussions is an important determinant of time-variations in the historical conduct of monetary policy with implications for asset returns. The period from mid-1996 to late2010 stands out as the time with the narrowest focus on balancing the dual mandate. Prior to the 1980s there was a outsized attention to employment and output growth considerations, while non dual-mandate discussions centered around financial stability considerations emerged after the Great Financial Crisis. Forward-looking financial stability concerns are a particularly important driver of a less accommodative monetary policy stance when Fed officials link these concerns to monetary policy, rather than changes in banking regulation. Conversely, discussions about current financial crises and monetary policy in the context of inflation-employment themes are associated with a more accommodative policy stance.

     

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    hdl: 10419/272874
    Schriftenreihe: Sveriges Riksbank working paper series ; 417
    Schlagworte: Natural Language Processing; Machine Learning; Central Bank Communication; Financial Stability; Zero Shot Classification; Extractive Question Answering; Semantic Textual Similarity
    Umfang: 1 Online-Ressource (circa 92 Seiten), Illustrationen
  23. Childhood circumstances and health of American and Chinese older adults
    a machine learning evaluation of inequality of opportunity in health
    Erschienen: [2024]
    Verlag:  Global Labor Organization (GLO), Essen

    Childhood circumstances may impact senior health, prompting this study to introduce novel machine learning methods to assess their individual and collective contributions to health inequality in old age. Using the US Health and Retirement Study (HRS)... mehr

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    Childhood circumstances may impact senior health, prompting this study to introduce novel machine learning methods to assess their individual and collective contributions to health inequality in old age. Using the US Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS), we analyzed health outcomes of American and Chinese participants aged 60 and above. Conditional inference trees and forest were employed to estimate the influence of childhood circumstances on self-rated health (SRH), comparing with the conventional parametric Roemer method. The conventional parametric Roemer method estimated higher IOP in health (China: 0.039, 22.67% of the total Gini coefficient 0.172; US: 0.067, 35.08% of the total Gini coefficient 0.191) than conditional inference tree (China: 0.022, 12.79% of 0.172; US: 0.044, 23.04% of 0.191) and forest (China: 0.035, 20.35% of 0.172; US: 0.054, 28.27% of 0.191). Key determinants of health in old age were identified, including childhood health, family financial status, and regional differences. The conditional inference forest consistently outperformed other methods in predictive accuracy as measured by out-of-sample mean squared error (MSE). The findings demonstrate the importance of early-life circumstances in shaping later health outcomes and stress the earlylife interventions for health equity in aging societies. Our methods highlight the utility of machine learning in public health to identify determinants of health inequality.

     

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    hdl: 10419/281669
    Schriftenreihe: GLO discussion paper ; no. 1384
    Schlagworte: Life Course; Inequality of Opportunity; Childhood Circumstances; Machine Learning; Conditional Inference Tree; Random Forest
    Umfang: 1 Online-Ressource (circa 29 Seiten), Illustrationen
  24. Classification of monetary and fiscal dominance regimes using machine learning techniques
    Erschienen: [2021]
    Verlag:  Institute for Monetary and Financial Stability, Goethe University Frankfurt, Frankfurt am Main

    This paper identiftes U.S. monetary and ftscal dominance regimes using machine learning techniques. The algorithms are trained and verifted by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017... mehr

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    This paper identiftes U.S. monetary and ftscal dominance regimes using machine learning techniques. The algorithms are trained and verifted by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning methods outperform a standard logistic regression concerning the simulated data. Among those the Boosted Ensemble Trees classifter yields the best results. We ftnd clear evidence of ftscal dominance before Volcker. Monetary dominance is detected between 1984-1988, before a ftscally led regime turns up around the stock market crash lasting until 1994. Until the beginning of the new century, monetary dominance is established, while the more recent evidence following the ftnancial crisis is mixed with a tendency towards ftscal dominance.

     

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    hdl: 10419/234736
    Schriftenreihe: Working paper series / Institute for Monetary and Financial Stability ; no. 160 (2021)
    Schlagworte: Monetary-fiscal interaction; Machine Learning; Classification; Markov-switching DSGE
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  25. Climate change impacts on crop yield
    development and evaluation of fundamental models as a basis for economic assessment
    Autor*in: Peichl, Michael
    Erschienen: [2021]
    Verlag:  Helmholtz Centre for Enviromental Research - UFZ, Leipzig

    Physical climate changes due to greenhouse gas emissions are well understood. However, quantifying the economic consequences remains a major challenge. Nevertheless, such quantification is crucial for the development of effective climate protection... mehr

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    Physical climate changes due to greenhouse gas emissions are well understood. However, quantifying the economic consequences remains a major challenge. Nevertheless, such quantification is crucial for the development of effective climate protection and adaptation strategies. Especially at local and regional levels, there is insufficient knowledge about the multiple impacts of climate change on economic sectors and regions. This is particularly true for the agricultural sector, which is considered to be vulnerable to the effects of global climate change. Since climate change not only changes temperature but also precipitation patterns in space and time, a higher variability of individual weather and the resulting extreme events (e.g. storms, flooding or droughts) is expected. Accurate models that depict the weather and crop yields are important not only for projecting the effects of agriculture, but also for projecting the impact of climate change on the associated economic and ecological consequences and thus for mitigation and adaptation policies. There are various methodological approaches to modelling climate impacts on agriculture. On the one hand, there are holistic approaches such as integrated assessment models. On the other hand, there are process-based or mechanistic models that capture the relevant biophysical relationships. Finally, there are empirical or statistical models that explain the relationship between meteorological variables and agricultural yields. These modelling approaches are rooted in very different disciplines and involve different emphases and assumptions, often resulting in a lack of consistency. Based on this scientific discussion, the thesis aims at the design of statistical approaches in order to allow a convergence of the results of the different methods. The aim is to identify missing aspects in current statistical approaches, such as the absence of important variables (e.g. soil moisture) and addressing the timing of the occurrence of extreme events that affect plant growth. In addition, new statistical approaches from the field of machine learning will be introduced to complement the existing methods, which are mainly based on econometrics. Furthermore, the approach presented here enables a Germany-wide impact assessment for the main crops. Finally, the development of such statistical damage functions promotes the management of the effects of extreme events on the agricultural sector on several time scales and can be used for climate change impact assessment. The work is cumulative and consists of three scientific articles.

     

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    Schriftenreihe: PHD Dissertation / Helmholtz Centre for Environmental Research ; 2021,2
    Schlagworte: Machine Learning; Agriculture; Crop Yield; Soil Moisture; Econometrics; Climate Change
    Umfang: 1 Online-Ressource (XIX, 106 Seiten, 2,14 MB), Illustrationen, Diagramme
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    Tag der Verteidigung: 07.12.2020

    Literaturverzeichnis: Seite 101-105

    Dissertation, Martin-Luther-Universität Halle-Wittenberg, 2020