This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms
This paper discusses the challenges faced by the empirical macroeconomist and methods for surmountin...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
We address the problem of selecting the common factors that are relevant for forecasting macroeconom...
This paper considers Bayesian variable selection in regressions with a large number of possibly high...
This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in ...
This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in ...
We address the problem of selecting the common factors that are relevant for forecasting macroeconom...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
The paper addresses the issue of forecasting a large set of variables using multivariate models. In ...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge reg...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Block factor methods offer an attractive approach to forecasting with many predictors. These extract...
This paper develops methods for automatic selection of variables in forecasting Bayesian vector auto...
This paper addresses the issue of improving the forecasting performance of vector autoregressions (V...
This paper discusses the challenges faced by the empirical macroeconomist and methods for surmountin...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
We address the problem of selecting the common factors that are relevant for forecasting macroeconom...
This paper considers Bayesian variable selection in regressions with a large number of possibly high...
This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in ...
This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in ...
We address the problem of selecting the common factors that are relevant for forecasting macroeconom...
We examine the issue of variable selection in linear regression modeling, where we have a potentiall...
The paper addresses the issue of forecasting a large set of variables using multivariate models. In ...
We examine the issue of variable selection in linear regression have a potentially large amount of ...
This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge reg...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Block factor methods offer an attractive approach to forecasting with many predictors. These extract...
This paper develops methods for automatic selection of variables in forecasting Bayesian vector auto...
This paper addresses the issue of improving the forecasting performance of vector autoregressions (V...
This paper discusses the challenges faced by the empirical macroeconomist and methods for surmountin...
This paper builds on a simple unified representation of shrinkage Bayes estimators based on hierarch...
We address the problem of selecting the common factors that are relevant for forecasting macroeconom...
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