Bootstrap aggregating or Bagging, introduced by Breiman (1996a), has been proved to be effective to improve on unstable forecast. Theoretical and empirical works using classification, regression trees, variable selection in linear and non-linear regression have shown that bagging can generate substantial prediction gain. However, most of the existing literature on bagging have been limited to the cross sectional circumstances with symmetric cost functions. In this paper, we extend the application of bagging to time series settings with asymmetric cost functions, particularly for predicting signs and quantiles. We link quantile predictions to binary predictions in a unified framwork. We find that bagging may improve the accuracy of unstable ...
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both...
Diferentes metodologias são propostas e exploradas com o intuito de reduzir o erro de previsão de sé...
After over two decades of extensive research on branch prediction, branch mispredictions are still a...
Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presen...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
Bootstrap aggregation, or bagging, is a prominent method used in statistical inquiry suggested to im...
This article shows that bagging can improve the forecast accuracy of time series models for realized...
This article explores the usefulness of bagging methods in forecasting economic time series from lin...
Bagging is a method of obtaining more ro- bust predictions when the model class under consideration ...
This paper explores the usefulness of bagging methods in forecasting economic time series from linea...
Abstract The literature on excess return prediction has considered a wide array of estimation scheme...
The literature on excess return prediction has considered a wide array of estimation schemes, among ...
We propose using the statistical method of Bagging to forecast the equity premium out-of-sample for ...
Exponential smoothing is one of the most popular forecasting methods. We present a tech-nique for bo...
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both...
Diferentes metodologias são propostas e exploradas com o intuito de reduzir o erro de previsão de sé...
After over two decades of extensive research on branch prediction, branch mispredictions are still a...
Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presen...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
A common problem in out-of-sample prediction is that there are potentially many relevant predictors ...
Bootstrap aggregation, or bagging, is a prominent method used in statistical inquiry suggested to im...
This article shows that bagging can improve the forecast accuracy of time series models for realized...
This article explores the usefulness of bagging methods in forecasting economic time series from lin...
Bagging is a method of obtaining more ro- bust predictions when the model class under consideration ...
This paper explores the usefulness of bagging methods in forecasting economic time series from linea...
Abstract The literature on excess return prediction has considered a wide array of estimation scheme...
The literature on excess return prediction has considered a wide array of estimation schemes, among ...
We propose using the statistical method of Bagging to forecast the equity premium out-of-sample for ...
Exponential smoothing is one of the most popular forecasting methods. We present a tech-nique for bo...
This paper provides a rigorous and detailed analysis of the methods of bagging, which addresses both...
Diferentes metodologias são propostas e exploradas com o intuito de reduzir o erro de previsão de sé...
After over two decades of extensive research on branch prediction, branch mispredictions are still a...