Search Results - subject:"bagging"

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Identifying demand elasticity via heteroscedasticity: A panel GMM approach to estimation and inference

von Brasch, Thomas; Raknerud, Arvid; Vigtel, Trond C. - 2024

This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity. While existing panel estimators address the simultaneity problem, the state-ofthe-art Feenstra/Soderbery (F/S) estimator suffers from inconsistency, inefficiency, and lacks a...

Persistent link: https://www.econbiz.de/10015097082

Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction

Liu, Zhenkun; Zhang, Ying; Abedin, Mohammad Zoynul; … - In: Journal of retailing and consumer services 79 (2024), pp. 1-17

Persistent link: https://www.econbiz.de/10015100820

Identifying demand elasticity via heteroscedasticity : a panel GMM approach to estimation and inference

Brasch, Thomas von; Raknerud, Arvid; Vigtel, Trond C. - 2024

Persistent link: https://www.econbiz.de/10015073836

Nowcasting GDP: what are the gains from machine learning algorithms?

Arro-Cannarsa, Milen; Scheufele, Rolf - 2024

Persistent link: https://www.econbiz.de/10015044842

Development of information technology for analyzing the customer churn of a telecommunication company

Papa, Andrii; Shemet, Yevgen; Yarovyi, Andrii; … - In: Technology audit and production reserves 2 (2022) 2/64, pp. 11-15

Persistent link: https://www.econbiz.de/10013325867

Predicting gold and silver price direction using tree-based classifiers

Sadorsky, Perry A. - In: Journal of Risk and Financial Management 14 (2021) 5, pp. 1-21

(bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds …. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much … more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient …

Persistent link: https://www.econbiz.de/10012611755

Application of bagging in day-ahead electricity price forecasting and factor augmentation

Özen, Kadir; Yıldırım, Dilem - 2021

Persistent link: https://www.econbiz.de/10013550327

Predicting gold and silver price direction using tree-based classifiers

Sadorsky, Perry A. - In: Journal of risk and financial management : JRFM 14 (2021) 5, pp. 1-21

Persistent link: https://www.econbiz.de/10012533982

Machine learning advances for time series forecasting

Masini, Ricardo P.; Medeiros, Marcelo C.; Mendes, Eduardo F. - 2020

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The...

Persistent link: https://www.econbiz.de/10012817069

Nagging predictors

Richman, Ronald; Wüthrich, Mario V. - In: Risks 8 (2020) 3, pp. 1-26

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

Persistent link: https://www.econbiz.de/10013200616