scholarly journals The Analysis of the Impact of Regulatory Environment on the Pace of Economic Growth of the World Countries According to the Bayesian Model Averaging

2013 ◽  
Author(s):  
Mariusz Prrchniak ◽  
Bartosz Witkowski
Keyword(s):  
Economic Growth ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Regulatory Environment ◽  
The World ◽  
The Impact

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 Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions

Epidemiology ◽  
2017 ◽  
Vol 28 (6) ◽  
pp. 889-897 ◽  
Author(s):  
Esra Kürüm ◽  
Joshua L. Warren ◽  
Cynthia Schuck-Paim ◽  
Roger Lustig ◽  
Joseph A. Lewnard ◽  
...  
Keyword(s):  
Public Health ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Health Interventions ◽  
Change Points ◽  
Public Health Interventions ◽  
The Impact

scholarly journals Bayesian Model Averaging with Stratified Sampling for Probabilistic Quantitative Precipitation Forecasting in Northern China during Summer 2010

2015 ◽  
Vol 143 (9) ◽  
pp. 3628-3641 ◽  
Author(s):  
Jiangshan Zhu ◽  
Fanyou Kong ◽  
Lingkun Ran ◽  
Hengchi Lei
Keyword(s):  
Logistic Regression ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Northern China ◽  
Training Sample ◽  
Stratified Sampling ◽  
Ensemble Spread ◽  
Skill Scores ◽  
The Impact

Abstract To study the impact of training sample heterogeneity on the performance of Bayesian model averaging (BMA), two BMA experiments were performed on probabilistic quantitative precipitation forecasts (PQPFs) in the northern China region in July and August of 2010 generated from an 11-member short-range ensemble forecasting system. One experiment, as in many conventional BMA studies, used an overall training sample that consisted of all available cases in the training period, while the second experiment used stratified sampling BMA by first dividing all available training cases into subsamples according to their ensemble spread, and then performing BMA on each subsample. The results showed that ensemble spread is a good criterion to divide ensemble precipitation cases into subsamples, and that the subsamples have different statistical properties. Pooling the subsamples together forms a heterogeneous overall sample. Conventional BMA is incapable of interpreting heterogeneous samples, and produces unreliable PQPF. It underestimates the forecast probability at high-threshold PQPF and local rainfall maxima in BMA percentile forecasts. BMA with stratified sampling according to ensemble spread overcomes the problem reasonably well, producing sharper predictive probability density functions and BMA percentile forecasts, and more reliable PQPF than the conventional BMA approach. The continuous ranked probability scores, Brier skill scores, and reliability diagrams of the two BMA experiments were examined for all available forecast days, along with a logistic regression experiment. Stratified sampling BMA outperformed the raw ensemble and conventional BMA in all verifications, and also showed better skill than logistic regression in low-threshold forecasts.

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 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 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 Building a Multimodel Flood Prediction System with the TIGGE Archive

2016 ◽  
Vol 17 (11) ◽  
pp. 2923-2940 ◽  
Author(s):  
Ervin Zsótér ◽  
Florian Pappenberger ◽  
Paul Smith ◽  
Rebecca Elizabeth Emerton ◽  
Emanuel Dutra ◽  
...  
Keyword(s):  
Land Surface ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Initial Conditions ◽  
Model Averaging ◽  
Surface Model ◽  
Prediction System ◽  
Flood Prediction ◽  
Forecast Performance ◽  
The Impact

Abstract In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.

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