Showing 1 - 10 of 11
of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …
Persistent link: https://www.econbiz.de/10011941488
of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable …
Persistent link: https://www.econbiz.de/10012146373
of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable …
Persistent link: https://www.econbiz.de/10012003693
operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The …
Persistent link: https://www.econbiz.de/10011557306
We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a...
Persistent link: https://www.econbiz.de/10010428185
Persistent link: https://www.econbiz.de/10011704738
of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …
Persistent link: https://www.econbiz.de/10011865621
Persistent link: https://www.econbiz.de/10011894611
optimizes all the parameters within the model. We employ Lasso and elastic-net penalty functions as regularization approach. The …
Persistent link: https://www.econbiz.de/10009779289
penalization parameter () of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization …
Persistent link: https://www.econbiz.de/10011598919