PEMODELAN AUTOREGRESIF SPASIAL MENGGUNAKAN BAYESIAN MODEL AVERAGING UNTUK DATA PDRB JAWA

Economic data always contains spatial effects. Gross Regional Domestic Product (GRDP) in Java is one of economic data that describes spatial dependence between adjacent districts/cities. The method that is suitable for modeling GDRP is spatial regression with spatial dependence on lags that is spatial autoregressive. GDRP prediction used the Bayesian Model Averaging (BMA) method. The ten autoregressive spatial model that have highest posterior probability was chosen to determined the BMA model by posterior probability. The explanatory variables used in this study were (1) mean years of schooling (2) life expectancy (3) income per capita (4) local revenue (5) number of workers (6) district minimum salary. The results showed that the number of workers was chosen as a predictor for the ten models. The model that have highest posterior probability probability is 0.54 which contains five explanatory variables that are mean years of schooling, income per capita, local revenue, number of workers and district minimum salary and the pseudo R2 of the model is 0.696.

Download Full-text

Bayesian Model Averaging and Weighted-Average Least Squares: Equivariance, Stability, and Numerical Issues

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x1201100402 ◽

2011 ◽

Vol 11 (4) ◽

pp. 518-544 ◽

Cited By ~ 36

Author(s):

Giuseppe De Luca ◽

Jan R. Magnus

Keyword(s):

Least Squares ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Weighted Average ◽

Simulated Data ◽

Linear Regression Models ◽

Explanatory Variables ◽

Memory Problems ◽

Regression Parameters

In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to several practical issues that users are likely to face in applied work: equivariance to certain transformations of the explanatory variables, stability, accuracy, computing speed, and out-of-memory problems. Performances of our bma and wals commands are illustrated using simulated data and empirical applications from the literature on model-averaging estimation.

Download Full-text

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

International Letters of Social and Humanistic Sciences ◽

10.18052/www.scipress.com/ilshs.43.10 ◽

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.

Download Full-text

IMPROVING NONLINEAR PROCESS MODELING USING MULTIPLE NEURAL NETWORK COMBINATION THROUGH BAYESIAN MODEL AVERAGING (BMA)

IIUM Engineering Journal ◽

10.31436/iiumej.v9i1.94 ◽

2010 ◽

Vol 9 (1) ◽

pp. 19-36 ◽

Cited By ~ 1

Author(s):

Zainal Ahmad ◽

Tang Pick Ha ◽

Rabiatul â€˜Adawiah Mat Noor

Keyword(s):

Process Modeling ◽

Bayesian Model ◽

Posterior Probability ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Training Data ◽

Likelihood Method ◽

Water Tank ◽

Model Generalization ◽

Testing Data

Improving model generalization of aggregated multiple neural networks for nonlinear dynamic process modeling using Bayesian Model Averaging (BMA) is proposed in this paper. Using BMA method, the posterior probability of a particular network being the true model is used as the combination weight for aggregating the network despite of using fixed combination weight as the model. The posterior probabilities are calculated using the sum square error (SSE) from the training data on each of the sample time, and tested to the testing data. The selections for the final weight are based on the least SSE calculated when each of the posterior probability is applied to the testing data. The likelihood method is employed for calculating the network error for each input data. Then, it is used to calculate the combination weight for the networks. Two non-linear dynamic system-modeling case studies are selected for this proposed method, which are water tank level prediction and pH neutralization process. Application result demonstrates that the combination using BMA technique can significantly improve model generalization compared to other linear combination approaches.

Download Full-text

The Determinants of Economic Growth in Iran Based on Bayesian Model Averaging

International Letters of Social and Humanistic Sciences ◽

10.18052/www.scipress.com/ilshs.49.1 ◽

2015 ◽

Vol 49 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Mohsen Mehrara ◽

Sadeq Rezaei

Keyword(s):

Economic Growth ◽

Labor Force ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Series Data ◽

Intermediate Goods ◽

Explanatory Variables ◽

Oil Revenue ◽

Determinants Of Economic Growth

This paper identifies the key determinants of economic growth in Iran, using annual time series data from 1974 to 2010. There is a very large literature on determinants of economic growth and several studies have included a large number of explanatory variables. Empirical models of economic growth are therefore plagued by problems of model uncertainty concerning the choice of explanatory variables and model specification. We utilize Bayesian Model Averaging (BMA) to resolve these model uncertainties. The results of this study indicate that the ratio of oil revenue to GDP is the most important variable affecting economic growth in the Iranian economy. Also the second and third effective variables on growth are respectively the ratio of imported capital and intermediate goods to GDP and labor force which lead to an increase in growth. Endogenous growth factors which are the factors contributing to the formation of human capital, not possess a large role in growth process. Therefore, the nature of Iran's economy has not endogenous and dynamic features and predominantly, economic growth has been made by injecting of exogenous sources (oil revenue, imported capital and intermediate goods, and labor force).

Download Full-text

Determinants of Renewable Energy Consumption among ECO Countries; Based on Bayesian Model Averaging and Weighted-Average Least Square

International Letters of Social and Humanistic Sciences ◽

10.18052/www.scipress.com/ilshs.54.96 ◽

2015 ◽

Vol 54 ◽

pp. 96-109 ◽

Cited By ~ 4

Author(s):

Mohsen Mehrara ◽

Sadeq Rezaei ◽

Davoud Hamidi Razi

Keyword(s):

Renewable Energy ◽

Energy Consumption ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Renewable Energy Sources ◽

Model Averaging ◽

Weighted Average ◽

Least Square ◽

Renewable Energy Consumption ◽

Explanatory Variables

Over recent years, renewable energy sources have emerged as an important component of world energy consumption. Increased concern over issues related to energy security and global warming suggests that in the future there will be a greater reliance on renewable energy sources. Given the role of renewable energy in the discussion of a reliable and sustainable energy future, it is important to understand its main determinants and to draw result implications for energy policy. This paper identifies the key determinants of renewable energy consumption among Economic Cooperation Organization (ECO) countries, over the period 1992-2011. There is a large literature on determinants of energy consumption and several studies have included a large number of explanatory variables. Empirical models of energy consumption are plagued by problems of model uncertainty concerning the choice of explanatory variables and model specification. We utilize Bayesian Model Averaging (BMA) and Weighted-Average Least Square (WALS) to resolve these model uncertainties. We have used not only conventional explanatory variables that have been used in last studies, but also institutional variables to consider the effect of socio-economic environment. The results of this study indicate that the institutional environment proxies, urban population, and human capital are the most important variables affecting renewable energy consumption in the ECO economies. Also the second and third effective variables are the renewables potential which lead to an increase in renewable energy consumption, and Co2 emission which has revers effect respectively. Therefore improving of institutional circumstances and human capital can be useful to renewable energy growth and reducing of detrimental externalities of fossil energy consumption.

Download Full-text