Long-Run Performance of Bayesian Model Averaging

2003 ◽  
Author(s):  
Adrian E. Raftery ◽  
Yingye Zheng
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Long Run

scholarly journals Determinants of Inflation in Iran Based on Bayesian Model Averaging (BMA)

2014 ◽  
Vol 43 ◽  
pp. 10-17
Author(s):  
Mohsen Mehrara ◽  
Arezoo Ghazanfari ◽  
Motahareh Alsadat Majdzadeh
Keyword(s):  
Exchange Rate ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Energy Price ◽  
Market Exchange ◽  
Explanatory Variables ◽  
Long Run ◽  
The Impact ◽  
Money Velocity

Due to the important influence of inflation on macro-economic variables, researchers pay tremendous amount of attention to its determinants. Accordingly, in the following research, the impact of 13 variables on inflation during the period of 1338-1391 by using Bayesian Model Averaging (BMA) method has been investigated for Iran economy. The ranking of the 13 explanatory variables are obtained based on the probability of their inclusion in model. The results show that the energy price and money imbalance (lagged ratio of money to nominal output) have expected and positive effect on inflation rate with a probability of 100 % and they are considered as the key explanatory variables in inflation equation. The energy price, money imbalance, money growth and market exchange rate growth have the first to fourth rank respectively. The influence of the production growth is not significant on the inflation in the short-run but it gradually influences the inflation through money imbalance channel in the long-run. In addition, most of the disinflation effects due to decrease in money supply will appear with delay. These results imply the dominance of monetary variables on inflation with cost push factors not having important impacts on prices. Also, oil revenue and imports influence the inflation through exchange rate channel, production and money velocity.

scholarly journals Which institutions are important for firms performance? Evidence from Bayesian model averaging analysis

2017 ◽  
Vol 64 (4) ◽  
pp. 383-400 ◽  
Author(s):  
Jarko Fidrmuc ◽  
Svatopluk Kapounek ◽  
Martin Siddiqui
Keyword(s):  
Political Institutions ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Financial Conditions ◽  
Long Run ◽  
Averaging Analysis ◽  
Business Regulations ◽  
Excessive Risk Taking ◽  
High Share

Using a rich dataset on individual firms in selected EU countries between 2005 and 2012, we document a surprisingly high share of assets tied in highly inefficient firms. Moreover, we discuss different channels through which institutions may affect firm financial developments and thus the long-run growth. Using Bayesian model averaging analysis, we discuss the importance of different types of economic, financial and political institutions. We show that high institutional quality improves the financial conditions of firms. However, too lax business regulations may worsen firms? performance possibly due to excessive risk taking behavior.

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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