Optimal model averaging forecasting in high-dimensional survival analysis

Bayesian Model Averaging and Forecasting

This paper focuses on the problem of variable selection in linear regression models. I briefly review the method of Bayesian model averaging, which has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. Some of the literature is discussed with particular emphasis on forecasting in economics. The role of the p...

Forecasting, Model Averaging and Model Selection

Abstract This paper explores forecasting using model selection and model averaging and attempts to draw conclusion both in the context of stationarity and non-stationarity. Model averaging tends to be viewed as a polar opposite of model selection; often the motivation for averaging is to avoid the pitfalls of selecting models. However, selection cannot be avoided since every possible model cann...

Forecasting Ination Using Dynamic Model Averaging

We forecast quarterly US in‡ation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We …nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and...

Model Averaging Estimation for Sparse High-dimensional Data

Sequential Model Averaging for High Dimensional Linear Regression Models

In high dimensional data analysis, we propose a sequential model averaging (SMA) method to make accurate and stable predictions. Specifically, we introduce a hybrid approach that combines a sequential screening process with a model averaging algorithm, where the weight of each model is determined by its Bayesian information (BIC) score (Schwarz, 1978; Chen and Chen, 2008). The sequential techni...