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Forecasting crude oil prices with a large set of predictors : Can LASSO select powerful predictors?

Zhang, Yaojie; Ma, Feng; Wang, Yudong - In: Journal of empirical finance 54 (2019), pp. 97-117

Persistent link: https://www.econbiz.de/10012174816

Predictor preselection for mixed-frequency dynamic factor models : a simulation study with an empirical application to GDP nowcasting

Franjic, Domenic; Schweikert, Karsten - In: Journal of forecasting 44 (2025) 2, pp. 255-269

Persistent link: https://www.econbiz.de/10015374018

The cross-section of Indian stock returns : evidence using machine learning

Lalwani, Vaibhav; Meshram, Vedprakash - In: Applied economics 54 (2022) 16, pp. 1814-1828

Persistent link: https://www.econbiz.de/10012875645

Forecasting crude oil returns with oil-related industry ESG indices

Li, Kaixin; Zhang, Zhikai; Wang, Yudong; Zhang, Yaojie - In: Journal of commodity markets : JCM 36 (2024), pp. 1-25

Persistent link: https://www.econbiz.de/10015162624

Macroeconomic forecasting using factor models and machine learning : an application to Japan

Maehashi, Kohei; Shintani, Mototsugu - 2020

Persistent link: https://www.econbiz.de/10013329759

Can machine learning help to select portfolios of mutual funds?

DeMiguel, Victor; Gil-Bazo, Javier; Nogales, Francisco J.; … - 2021 - This version: March 24, 2021

Persistent link: https://www.econbiz.de/10012819375

Forecasting financial and macroeconomic variables using data reduction methods : new empirical evidence

Kim, Hyun Hak; Swanson, Norman R. - In: Journal of econometrics 178 (2014) 1, pp. 352-367

Persistent link: https://www.econbiz.de/10010256842

Predicting exchange rates out of sample : can economic fundamentals beat the random walk?

Li, Jiahan; Tsiakas, Ilias; Wang, Wei - In: Journal of financial econometrics : official journal of … 13 (2015) 2, pp. 293-341

Persistent link: https://www.econbiz.de/10011339312

Iassopack : model selection and prediction with regularized regression in stata

Ahrens, Achim; Hansen, Christian Bailey; Schaffer, Mark E. - 2019

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...

Persistent link: https://www.econbiz.de/10011972491

Evaluating the accuracy of valuation multiples on Indian firms using regularization techniques of penalized regression

Gupta, Vandana - In: Theoretical economics letters 9 (2019) 1, pp. 180-209

Persistent link: https://www.econbiz.de/10012005386