scholarly journals Sources of Economic Growth: A Global Perspective

2019 ◽  
Vol 11 (1) ◽  
pp. 275 ◽  
Author(s):  
Marcin Błażejowski ◽  
Jacek Kwiatkowski ◽  
Jakub Gazda
Keyword(s):  
Economic Growth ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Global Perspective ◽  
Global Level ◽  
Average Growth Rate ◽  
Average Growth ◽  
Potential Factors ◽  
Bayesian Averaging

The main goal of this paper is to determine the factors responsible for economic growth at the global level. The indication of the sources of economic growth may be an important element of the sustainable economic policy for development. The novelty of this research lies in employing an analysis based on data, which consist of an average growth rate of the Gross Domestic Product (GDP) for 168 countries for the years 2002–2013. The Bayesian model averaging approach is used to identify potential factors responsible for differences in countries’ GDPs. Additionally, a jointness analysis is performed to assess the potential independence, substitutability, and complementarity of the factors of economic growth. The robustness of the results is confirmed by Bayesian averaging of classical estimates. We identify the most probable factors of economic growth, and we find that the most important determinants are variables associated with the so-called “Asian development model”.

scholarly journals Human Capital and Economic Growth in Morocco: Evidence from Bayesian Model Averaging

2017 ◽  
Vol 10 (12) ◽  
pp. 167 ◽  
Author(s):  
Omar Essardi ◽  
Redouane Razzouk
Keyword(s):  
Economic Growth ◽  
Human Capital ◽  
Life Expectancy ◽  
Granger Causality ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Granger Causality Test ◽  
Causality Test

The paper investigates the relationship between human capital and economic growth in Morocco during the period from 1965 to 2015. In order to test this relationship we estimated a growth function using firstly the Johansen multivariate cointegration test and the Granger causality test. Secondly, we used the method of the Bayesian Model Averaging (BMA) that takes into consideration the uncertainty related to the specification of the model studied. In the theoretical literature, the difficulty of measuring human capital is often stressed. In order to overcome this problem, we use four proxies of human capital: first, we employ the average years of schooling. Second, we use the index of the gap in life expectancy between Morocco and developed countries. Third, we integrate the qualitative aspects of education and health by constructing two composite indicators of human capital using Principal Component Analysis (PCA) method.The main results of regression analysis confirm that in the specification of determinants of GDP per worker the average years of total schooling, the life expectancy index and the indicator of quality of health affect positively and significantly level of GDP per worker. However, in the specification of determinants of the growth of the GDP per worker, we found there is no proxy of human capital that affects significantly the growth of the GDP per worker.In addition, the results of Granger causality test show that only the indicator of quality of health that cause the GDP per worker. As well, these results show that the average years of total schooling and the indicator of quality of education cause the growth of GDP per worker. We suggest that the Moroccan authorities should make additional efforts to raise the level of quality of human capital especially in the health sector and increase the productivity of both public and private investment.

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

2015 ◽  
Vol 49 ◽  
pp. 1-11 ◽  
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).

scholarly journals BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 21
Author(s):  
Marcin Błażejowski ◽  
Jacek Kwiatkowski ◽  
Paweł Kufel
Keyword(s):  
Parallel Processing ◽  
Model Selection ◽  
Money Demand ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Automatic Model Selection ◽  
Forecasting Performance ◽  
Bayesian Averaging

In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation. Empirical results verify the correctness of BACE and BMA selection and exhibit similar or better forecasting performance compared with a non-pooling approach. As a benchmark, we use Autometrics—an algorithm for automatic model selection. Our study is implemented in the easy-to-use gretl packages, which support parallel processing, automates numerical calculations, and allows for efficient computations.

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
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