Granger causality between energy use and economic growth in France with using geostatistical models

This paper introduces a new way for investigating linear and nonlinear Granger causality between energy use and economic growth in France over the period 1960_2005 with using geostatistical models (kiriging and IDW). This approach imitates the Granger definition and structure and also, improves it to have better ability for probe nonlinear causality. Results of both VEC and Improved-VEC (with geostatistical methods) are almost same. Both show the existence of long run unidirectional causality from energy consumption to economic growth. The geostatistical analyzing shows there are some Exponential functions in VEC structure instead of linear form.Granger causality; Energy consumption; GDP; Geostatistical model; France

Forecasting the role of public expenditure in economic growth Using DEA-neural network approach

This paper integrates data envelopment analysis (DEA) and artificial neural networks (ANN) to forecast the role of public expenditure in economic growth in OCDE countries. The results show that this approach is a powerful and appropriate method to forecast this role. DEA method allows us to develop a neutral evaluation, unbiased a priori by any type of criteria, of the proportions in which the goal of productive spending is pursued, for any expenditure. Then we apply ANN to forecast economic growth by using input data taken at frontier. At the end of the DEA-ANN chain, prediction-power tests appear positive: best structures of multiple hidden layers indicate more ability to forecast according to best structures of single hidden layer but the difference between those is not much.DEA method; Economic growth; Public expenditure; Artificial neural network; OCDE countries

Relationship between exports, imports, and economic growth in France: evidence from cointegration analysis and Granger causality with using geostatistical models

This paper introduces a new way of investigating linear and nonlinear Granger causality between exports, imports and economic growth in France over the period 1961-2006 with using geostatistical models (kiriging and inverse distance weighting). Geostatistical methods are the ordinary methods for forecasting the locations and making map in water engineerig, environment, environmental pollution, mining, ecology, geology and geography. Although, this is the first time which geostatistics knowledge is used for economic analyzes. In classical econometrics there do not exist any estimator which have the capability to find the best functional form in the estimation. Geostatistical models investigate simultaneous linear and various nonlinear types of causality test, which cause to decrease the effects of choosing functional form in autoregressive model. This approach imitates the Granger definition and structure but improve it to have better ability to investigate nonlinear causality. Results of both VEC and Improved-VEC (with geostatistical methods) are similar and show existance of long run unidirectional causality from exports and imports to economic growth. However the F-statistic of improved-VEC is larger than VEC indicating that there are some exponential and spherical functions in the VEC structure instead of the linear form.Granger causality; Exports; Imports; Economic growth; Geostatistical model; Kiriging; Inverse distance weighting; Vector auto-regression; France

Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method

This paper, we studied the ability of geostatistical models (ordinary kriging (OK) and Inverse distance weighting (IDW)), adaptive neuro-fuzzy inference system (ANFIS) and Winter method for prediction of seasonality in prices of potatoes and onions in Iran over the seasonal period 1986_2001. Results show that the best estimators in order are winter method, ANFIS and geostatistical methods. The results indicate that Winter and ANFIS had powerful results for prediction the prices while geostatistical models were not useful in this respect.Price; Geostatistical model; Kiriging; Inverse distance weighting; Winter’s method; Adaptive neuro fuzzy inference system; Potatoes; Onions; Iran

Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method

Price, Geostatistical model, Kiriging, Inverse distance weighting, Winter’s method, Adaptive neuro fuzzy inference system, Potatoes, Onions, Iran, Crop Production/Industries, Demand and Price Analysis,

Causality test between health care expenditure and GDP in US: comparing periods

In the literature dedicated to the "health as a luxury good" question, health care expenditure (HCE) is hypothesized to be a function of GDP without considering any other relationships. In this paper, we argue that this could be a bilateral relationship: good health is considered as an input of the macroeconomic production function, stimulating the GDP. A modified version of the Granger (1969) causality test proposed by Toda and Yamamoto (1995) is investigated between GDP per capita and HCE per capita in United States for comparing the periods of 1965_1984, 1975_1994, 1985_2004 and 1965_2004. Results show these three periods have different causal relationships. At the beginning for 1965_1984, there exists a bilateral relationship. For the following period, there is a unidirectional relationship from HCE to GDP, and for the 1985_2004, a unidirectional GDP_HCE is significant. From the start to end of periods (1965_2004), a unidirectional relation from HCE to GDP is existed

Causality test between health care expenditure and GDP in US: comparing periods

In the literature dedicated to the "health as a luxury good" question, health care expenditure (HCE) is hypothesized to be a function of GDP without considering any other relationships. In this paper, we argue that this could be a bilateral relationship: good health is considered as an input of the macroeconomic production function, stimulating the GDP. A modified version of the Granger (1969) causality test proposed by Toda and Yamamoto (1995) is investigated between GDP per capita and HCE per capita in United States for comparing the periods of 1965_1984, 1975_1994, 1985_2004 and 1965_2004. Results show these three periods have different causal relationships. At the beginning for 1965_1984, there exists a bilateral relationship. For the following period, there is a unidirectional relationship from HCE to GDP, and for the 1985_2004, a unidirectional GDP_HCE is significant. From the start to end of periods (1965_2004), a unidirectional relation from HCE to GDP is existed.Health care expenditure per capita; per capital GDP; Toda-Yamamoto causality; United States

Nursing graduates and quality of acute hospital care in 33 OECD countries: Evidence from Generalized Linear Models and Data Envelopment Analysis

BackgroundThere is a lack of cross-national research to examine the role of new graduate nurses in improving the quality of nursing care and patient outcomes.PurposeTo measure the role and clinical effectiveness of new graduate nurses in improving the quality of acute hospital care in the members of Organisation for Economic Co-operation and Development (OECD).MethodsThe total number of nursing graduates per 100,000 population and three OECD’s Health Care Quality Indicators (HCQI) in acute care including 30-day in-hospital and out-of-hospital mortality rates per 100 patients based on acute myocardial infarction (MORTAMIO), hemorrhagic stroke (MORTHSTO) and ischemic stroke (MORTISTO) were collected in 33 OECD countries. Four control variables including the number of medical graduates, practicing nurses and doctors densities per 1000 population (proxies for other health professions) and the total number of Computed Tomography scanners per one million population (proxy of medical technology level) were added in investigations. The statistical technique of Generalized Linear Models (GLM) and Data Envelopment Analysis (DEA) were used in data analysis.ResultsResults of GLM confirm the existence of meaningful association between the density of nursing graduates and improving the quality of acute care i.e. a 1% rise in the number of nursing graduates in year 2015 reduced MORTAMIO, MORTHSTO and MORTISTO by 1.11%, 0.08% and 0.46%, respectively. According to the result of DEA, clinical effectiveness of new graduate nurses – i.e. reaching the higher clinical outcomes with the same staffing level – in reducing mortality rates in patients with life-threatening conditions were at highest level in Luxembourg, Finland, Japan, Italy, Norway, Sweden and Switzerland.ConclusionsHigher staffing level of new graduate nurses associates with better patient outcomes in acute care, although the clinical effectiveness of nursing graduates – associated with the level of education and practice – is the determinant factor of improving the quality of acute hospital care and patient survival rates in OECD.</p

A new approach for estimation of long-run relationships in economic analysis using Engle-Granger and artificial intelligence methods

In time series analysis, most estimation of relationships and tests are typically based on linear estimators and most classical co-integration methods and causality tests are based on OLS regresses. However the linear functional specification is not necessarily the most appropriate form. This paper breaks the ordinary rules in econometrics and makes use of time series with artificial intelligence methods, testing for existence of nonlinear relationship. We illustrate the testing exercise using two examples based on OECD health data. In our illustration we confirm that improved nonlinear AEG and VEC, significantly, have a better ability to identify long run co-integration and causal relationships than ordinary linear ones. Ordinary methods and improved-nonlinear methods demonstrate similar results if the variables in a model are approximately linear

Forecasting the role of public expenditure in economic growth Using DEA-neural network approach

This paper integrates data envelopment analysis (DEA) and artificial neural networks (ANN) to forecast the role of public expenditure in economic growth in OCDE countries. The results show that this approach is a powerful and appropriate method to forecast this role. DEA method allows us to develop a neutral evaluation, unbiased a priori by any type of criteria, of the proportions in which the goal of productive spending is pursued, for any expenditure. Then we apply ANN to forecast economic growth by using input data taken at frontier. At the end of the DEA-ANN chain, prediction-power tests appear positive: best structures of multiple hidden layers indicate more ability to forecast according to best structures of single hidden layer but the difference between those is not much