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Best subset selection via cross-validation criterion

Takano, Yuichi; Miyashiro, Ryuhei - In: Top : an official journal of the Spanish Society of … 28 (2020) 2, pp. 475-488

Persistent link: https://www.econbiz.de/10012237002

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

Kim, Hyun Hak; Swanson, Norman R. - 2011

methods Our results suggest that model averaging does not dominate other well designed prediction model specification methods … using recursive estimation windows, which dominate other windowing approaches in our experiments, prediction models …-spread variables in nonlinear prediction specification. …

Persistent link: https://www.econbiz.de/10010282841

Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods

Kim, Hyun Hak; Swanson, Norman R. - In: International journal of forecasting 34 (2018) 2, pp. 339-354

Persistent link: https://www.econbiz.de/10012030940

Looking into the black box of the Korean economy : the sparse factor model approach

Kim, Hyun Hak - In: Journal of the Asia Pacific economy 23 (2018) 1, pp. 1-16

Persistent link: https://www.econbiz.de/10011959151

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

Asymptotic optimality of full cross-validation for selecting linear regression models

Droge, Bernd - 1997

For the problem of model selection, full cross-validation has been proposed as alternative criterion to the traditional cross-validation, particularly in cases where the latter one is not well defined. To justify the use of the new proposal we show that under some conditions, both criteria share...

Persistent link: https://www.econbiz.de/10010310761

Conditional variance forecasts for long-term stock returns

Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks 7 (2019) 4, pp. 1-22

squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

Persistent link: https://www.econbiz.de/10013200531

Machine learning for forecasting excess stock returns : the five-year-view : a preprint

Kyriakou, Ioannis; Mousavi, Parastoo; Nielsen, Jens Perch; … - 2019

Persistent link: https://www.econbiz.de/10012138036

Conditional variance forecasts for long-term stock returns : a preprint

Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - 2019

Persistent link: https://www.econbiz.de/10012138047

Conditional variance forecasts for long-term stock returns

Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks : open access journal 7 (2019) 4/113, pp. 1-22

squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

Persistent link: https://www.econbiz.de/10012127861

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