scholarly journals Forecast Accuracy and Economic Gains from Bayesian Model Averaging Using Time Varying Weights

2009 ◽  
Author(s):  
Lennart F. Hoogerheide ◽  
Richard Kleijn ◽  
Francesco Ravazzolo ◽  
H. K. van Dijk ◽  
Marno Verbeek
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Forecast Accuracy ◽  
Model Averaging ◽  
Time Varying

scholarly journals Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights

2010 ◽  
Vol 29 (1-2) ◽  
pp. 251-269 ◽  
Author(s):  
Lennart Hoogerheide ◽  
Richard Kleijn ◽  
Francesco Ravazzolo ◽  
Herman K. Van Dijk ◽  
Marno Verbeek
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Forecast Accuracy ◽  
Model Averaging ◽  
Time Varying

scholarly journals Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

2019 ◽  
Vol 11 (19) ◽  
pp. 5305 ◽  
Author(s):  
Krzysztof Drachal
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Probability Model ◽  
Model Averaging ◽  
Time Varying ◽  
Agricultural Commodities ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Dynamic Model Averaging ◽  
Commodities Prices

In the described research three agricultural commodities (i.e., wheat, corn and soybean) spot prices were analyzed. In particular, one-month ahead forecasts were built with techniques like dynamic model averaging (DMA), the median probability model and Bayesian model averaging. The common features of these methods are time-varying parameters approach toward estimation of regression coefficients and dealing with model uncertainty. In other words, starting with multiple potentially important explanatory variables, various component linear regression models can be constructed. Then, from these models an averaged forecast can be constructed. Moreover, the mentioned techniques can be easily modified from model averaging into a model selection approach. Considering as benchmark models, time-varying parameters regression with all considered potential price drivers, historical average, ARIMA (Auto-Regressive Integrated Moving Average) and the naïve forecast models, the Diebold–Mariano test suggested that DMA is an interesting alternative model, if forecast accuracy is the aim. Secondly, the interpretation of time-varying weights ascribed to component models containing a given variable suggested that economic development of emerging BRIC economies (Brazil, Russia, India and China) is recently one of the most important drivers of agricultural commodities prices. The analysis was made on the monthly data between 1976 and 2016. The initial price drivers were various fundamental, macroeconomic and financial factors.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

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