Is health care a necessary or luxury product for Asian countries An answer using panel approach.With a view to determining the appropriate estimation method, we need to check the stationary of the variables and also their order of integration.However, cross sectional dependence or cross sectional correlation of the variables is a fact that should be detected for the variables to decide which panel unit root test would be applied.Table 1 contains the test results for Cross Sectional Dependence of different variables, and suggests that it is possible to reject the null hypothesis for all the variables at 1 level of significance.Therefore, the residuals from the standard panel regression would be contemporaneously correlated and this should be addressed while panel stationarity would be tested.Table 1. Test Results for Cross Sectional Dependence of the Variables.Therefore, we have used IPS panel unit root test to detect the stationarity of the variables along with some other tests e.Levin, Lin and Chu LLC test Levin et al., 2.ADF Fisher Test and PP Fisher test Choi, 3.Table 2 and Tables 7, 8 and 9 in Appendix contain the panel unit root test results for each of the variables.All the tests are concerned with the null hypothesis of Panels Contain Individual Unit Root except LLC that tests the null hypothesis of Panel Contains Common Unit Root.EViews9/overview/panel_wfs.png' alt='Serial Correlation In Panel Data Eviews' title='Serial Correlation In Panel Data Eviews' />A time series is a series of data points indexed or listed or graphed in time order.Most commonly, a time series is a sequence taken at successive equally spaced.EViews offers academic researchers, corporations, government agencies, and students access to powerful statistical, forecasting, and modelling tools through an.Serial Correlation In Panel Data Eviews' title='Serial Correlation In Panel Data Eviews' />In this paper, new approaches are taken to explore two new dimensions of the oil growth nexus that are relevant when focusing on oil producing countries.Based on. The tests have been carried out with two different test regression specifications one with constant and the other with constant and trend.It is evident from the test results that GDP and HCE are difference stationary i.I1 variable according to all tests.With regards to infant mortality, it is also found to be difference stationary in intercept and trend specification under IPS, ADF Fisher and PP Fisher test.Thus it can be treated as an I1 variable.Life expectancy was found to be difference stationary in intercept specification under IPS, in intercept and trend specification under LLC and in both specification under ADF Fisher test.Since all the variables have been found to be integrated of a unique order we have identified the long run relationship among them by establishing the panel cointegration.Table 2. Panel Unit Root Test Results of the Variables.In order to check the existence of cointegration among the variables along with Pedroni 3.Engle Granger based panel cointegration test, we applied Westerlund 3. Install Mod Proxy Apache Centos Install . The later one is already established in the literature for its robustness against panel with heterogeneity and cross sectional dependence.Hence, application of this test allowed us to check issue of existence of cointegartion among health care expenditure and income while controlling for health status improvement measured with infant mortality and life expectancy in a more comprehensive manner.Both the tests have been performed with three different deterministic specifications.Table 3 and Table 1.Appendix contain the test results of panel cointegration.For testing the cointegration among health care expenditure, income and infant mortaility when neither constant nor trend was used as deterministic specification, the null hypothesis of no cointegration was rejected by all 1.Pedroni 3. 1, 3. All the 4 statistic of Westerlund 3.In the same specification when cointegration was checked among health care expenditure, income and life expectancy a total of 7 statistic in Pedroni 3.Westerlund 3. 5 was found to be statistically significant.When the deterministic specification was changed to allow the presence of constant, only 7 and 6 of 1.Pedroni 3. 1, 3. By using the similar deterministic specification Westerlund 3.The conclusion of Westerlund 3.While Pedroni 3. Thus, it can be argued that there might exists a long run cointegrating relationship among health expenditure, income and health status improvement which is substituted by using infant mortality and life expectancy.Table 3. Westerlund Panel Cointegration Test.With a view to estimate the cointegrating vector we have applied two different methods, FMOLS and DOLS.Each of the methods provides three different estimators namely, pooled, pooled weighted and grouped mean.The pooled FMOLS estimator is the extension of Phillips and Hansen 3.FMOLS estimator offered by Phillips and Moon 4.In order to allow different long run variances across the cross section for heterogeneous panels, Pedroni 4.Kao and Chiang 4.FMOLS. Finally the grouped mean FMOLS estimator developed by Pedroni 4.FMOLS estimates. In contrast to FMOLS, augmentation of model with lags and leads of differenced regressand and regressors in DOLS helps it to overcome the problem of asymptotic endogeneity and serial correlation.Kao and Chiang 4.Mark and Sul 4. 4, 4.Pedroni 4. 3 extended the standard DOLS estimation developed by Saikkonen 3.Stock and Watson 3.Kao and Chiang 4.DOLS where the augmented cointegrating regression allows the short run dynamics to be cross section specific.By allowing heterogenous long run variance, Mark and Sul 4.DOLS. Finally, Pedroni 4.DOLS estimates by averaging the individual cross section DOLS estimates.Table 4 contains the estimation results of long run relationship between health care expenditure and GDP and infant mortality as a measure of health status improvement.It is evident that the elasticity of health care expenditure with respect to income in Asian countries is less than unit.The elasticity coefficient varies from 0.FMOLS while the range was found to be 0.DOLS method. The Wald test was observed to be significant in all cases.Thus, health care in Asian countries can be argued as a necessary and normal product.The elasticity coefficient of infant mortality was found to be negative and significant in almost all cases.Therefore, there is an inverse relationship between health care expenditure and infant mortality in Asian countries.Thus, the higher the health care expenditure, the lower the infant mortality or vice versa.Table 4. Estimation of Cointegrating Regression with Infant Mortality as proxy for Health Status Improvement.Table 5 contains the results of cointegrating relationship between health care expenditure and GDP and another indicator of health status improvement measured by life expectancy.The elasticity coefficient of health care expenditure with respect of income was also found to be less than one.Here the elasticity measure varied in between 0.FMOLS method and 0.DOLS method. The coefficient has been observed to remain positive and significant in all cases.Thus, for Asian countries health care expenditure increases less than proportionately with the increase in income.The coefficient of life expectancy was found to be significant in almost all cases.Therefore, with respect to increase in life expectancy, health care expenditure increases in these countries.Table 5. Estimation of Cointegrating Regression with Life Expectancy as proxy for Health Status Improvement.Thus, unlike many OECD and developed countries such as USA, Canada, Germany and Italy where health care expenditure has been identified as luxury good, for Asian Countries it is revealed to be a necessary one.The findings contradict Hassan et.Dreger and Reimers 5, Mehrara et.
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