An application of Bayesian Model Averaging, BMA, is implemented to construct combined forecasts for the colombian inflation for the short and medium run. A model selection algorithm is applied over a set of linear models with a large dataset of potencia
The standard methodology when building statistical models has been to use one of several algorithms ...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Recently, there has been a broadening concern on forecasting techniques that are applied on large da...
Recent empirical work has considered the prediction of inflation by combining the information in a l...
Recent empirical work has considered the prediction of inflation by combining the information in a l...
This paper considers the problem of forecasting in dynamic factor models using Bayesian model averag...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model a...
Forecasting of inflation has become crucial for both policy makers and private agents who try to und...
With the concept of trend inflation now widely understood as to be important as a measure of the pub...
Typically, central banks use a variety of individual models (or a combination of models) when foreca...
Three methodologies of estimation of models with many predictors are implemented to forecast Colombi...
This paper examines how vector autoregression model by Bayesian model averaging method can improve f...
This document reviews and applies recently developed techniques for Bayesian estimation and model se...
The standard methodology when building statistical models has been to use one of several algorithms ...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
Recently, there has been a broadening concern on forecasting techniques that are applied on large da...
Recent empirical work has considered the prediction of inflation by combining the information in a l...
Recent empirical work has considered the prediction of inflation by combining the information in a l...
This paper considers the problem of forecasting in dynamic factor models using Bayesian model averag...
Abstract. Standard statistical practice ignores model uncertainty. Data analysts typically select a ...
This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model a...
Forecasting of inflation has become crucial for both policy makers and private agents who try to und...
With the concept of trend inflation now widely understood as to be important as a measure of the pub...
Typically, central banks use a variety of individual models (or a combination of models) when foreca...
Three methodologies of estimation of models with many predictors are implemented to forecast Colombi...
This paper examines how vector autoregression model by Bayesian model averaging method can improve f...
This document reviews and applies recently developed techniques for Bayesian estimation and model se...
The standard methodology when building statistical models has been to use one of several algorithms ...
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from...
This paper focuses on the problem of variable selection in linear regression models. I briefy revie...
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