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Year of publication
Subject
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Artificial intelligence 6 Künstliche Intelligenz 6 machine learning methods 5 Credit risk 2 Forecasting model 2 Inflation nowcasting 2 Kreditrisiko 2 Prognoseverfahren 2 mixed-frequency modeling 2 scanner price data 2 Ausreissererkennung 1 Bank 1 Bank Marketing 1 Bankgeschäft 1 Banking services 1 Causality analysis 1 Chi-square 1 Consumer price index 1 Credit default swaps 1 Credit derivative 1 Credit rating 1 Data Mining 1 Data mining 1 Deutschland 1 Economic indicator 1 Estimation theory 1 Feature Selection 1 Germany 1 Inflation 1 Inflation rate 1 Inflationsrate 1 Information Gain 1 Kausalanalyse 1 Konjunkturindikatoren 1 Kreditderivat 1 Kreditwürdigkeit 1 Machine Learning Methods 1 Machine Learning methods 1 Machine learning methods 1 Microfinance 1
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Online availability
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Free 8 CC license 2
Type of publication
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Book / Working Paper 5 Article 3
Type of publication (narrower categories)
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Graue Literatur 4 Non-commercial literature 4 Working Paper 3 Arbeitspapier 2 Article in journal 2 Aufsatz in Zeitschrift 2 Aufsatzsammlung 1 Hochschulschrift 1
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Language
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English 7 Undetermined 1
Author
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Beck, Günter W. 2 Carstensen, Kai 2 Menz, Jan-Oliver 2 Schnorrenberger, Richard 2 Wieland, Elisabeth 2 Almirall, Daniel 1 Bulhakova, Olha 1 Burgette, Lane F. 1 Christiansen, Tobias G. 1 Griffin, Beth Ann 1 Hu, Nan 1 Kakilli Acaravci, Songul 1 Kostenko, Victoria 1 Li, Jian 1 McCaffrey, Daniel F. 1 Meyer Cirkel, Alexis 1 Parlar, Tuba 1 Rudyanova, Tatyana 1 Setodji, Claude Messan 1 Ulianovska, Yuliia 1 Weeks, Melvyn 1
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Published in...
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Cambridge working papers in economics 1 ECB Working Paper 1 IMF working papers 1 International journal of economics and financial issues : IJEFI 1 Journal of Causal Inference 1 Technology audit and production reserves 1 Working paper series / European Central Bank 1
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Source
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ECONIS (ZBW) 6 EconStor 1 Other ZBW resources 1
Showing 1 - 8 of 8
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Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany
Beck, Günter W.; Carstensen, Kai; Menz, Jan-Oliver; … - 2024
We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation. Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At...
Persistent link: https://www.econbiz.de/10014543640
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Cover Image
Nowcasting consumer price inflation using high-frequency scanner data : evidence from Germany
Beck, Günter W.; Carstensen, Kai; Menz, Jan-Oliver; … - 2024
We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation. Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At...
Persistent link: https://www.econbiz.de/10014527067
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Statistical methods for early estimation of short term indicators
2024
Economic indicators provide important information for the evaluation of the state of the economy of a country and they are used for business cycle analysis. Timeliness of the indicators is of fundamental interest for analysts of government authorities, national banks, politicians or economic...
Persistent link: https://www.econbiz.de/10015192716
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Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients
Bulhakova, Olha; Ulianovska, Yuliia; Kostenko, Victoria; … - In: Technology audit and production reserves 4 (2022) 2/66, pp. 14-18
Persistent link: https://www.econbiz.de/10013459326
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Distributional aspects of microcredit expansions
Christiansen, Tobias G.; Weeks, Melvyn - 2020
Persistent link: https://www.econbiz.de/10013206331
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Completing the market : generating shadow CDS spreads by machine learning
Hu, Nan; Li, Jian; Meyer Cirkel, Alexis - 2019
ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and …-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add …
Persistent link: https://www.econbiz.de/10012155143
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Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models
Griffin, Beth Ann; McCaffrey, Daniel F.; Almirall, Daniel; … - In: Journal of Causal Inference 5 (2017) 2
Abstract: In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance...
Persistent link: https://www.econbiz.de/10014610835
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Using data mining techniques for detecting the important features of the bank direct marketing data
Parlar, Tuba; Kakilli Acaravci, Songul - In: International journal of economics and financial issues … 7 (2017) 2, pp. 692-696
Persistent link: https://www.econbiz.de/10011809107
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