Variable Selection in Macroeconomic Forecasting with Many Predictors

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods o¤er an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors re‡ecting di¤erent blocks of variables (e.g. a price block, a housing block, a …nancial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodo...

Macroeconomic Forecasting Using Many Predictors July 2000 ( Revised August 15 , 2000 )

Bayesian Variable Selection with Related Predictors

In data sets with many predictors, algorithms for identifying a good subset of predic-tors are often used. Most such algorithms do not account for any relationships between predictors. For example, stepwise regression might select a model containing an interaction AB but neither main eeect A or B. This paper develops mathematicalrepresentations of this and other relations between predictors, wh...

Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ...

Model Selection Approach to Real- Time Macroeconomic Forecasting