Online Program Home
  My Program

Abstract Details

Activity Number: 338 - Time Series and Forecasting
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #324235 View Presentation
Title: Time Series Prediction and Predictor Selection in High-Dimensional Quantile Autoregressive Regression
Author(s): Dawit Zerom*
Companies: California State University at Fullerton
Keywords: Conditional Quantile ; Prediction ; High Dimensional ; QAR ; Penalized ; FRED database

With the purpose of multi-step ahead out-of-sample quantile prediction as well as exogenous predictor selection, we introduce a flexible semiparametric quantile regression model that logically extends the quantile autoregressive (QAR) model by allowing arbitrary smooth (possibly nonlinear) functions of high dimensional (possibly larger than the sample size) exogenous predictors. For such partially linear QAR model and assuming exogenous predictor quantile sparsity, we develop a flexible a practical framework to approximate the quantile prediction as well as select relevant exogenous predictors. We also provide an application to U.S. inflation out-of-sample prediction using a set of 134 monthly macroeconomic exogenous predictors variables based on the FRED database.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association