Can environmental sustainability be decoupled from economic growth? Empirical evidence from Eastern Europe using the common correlated effect mean group test

The European Union(EU) and Organisation for Economic Co-operation and Development(OECD) aim to develop long-term policies for their respective member countries. Having observed increasing dangers to the environment posed by rising economic growth, they are seeking pathways to enable policy action on economic growth and environmental sustainability. Given the facts in theoretical and empirical studies, this study assessed the validity of the decoupling hypothesis by investigating asymmetricity in the relationship between environmental sustainability and economic growth in nine Eastern European countries from 1998 to 2017 using the cross-section augmented Dickey-Fuller(CADF) unit root, panel corrected standard error(PCSE), common correlated effect mean group(CCEMG), and Dumitrescu Hurlin causality approaches. Both population growth and drinking water are used as controlled variables. The outcomes establish strong cointegration among all the variables of interest. According to the results of CCEMG test, economic growth exerts short-term environmental degradation but has long-term environmental benefits in Eastern Europe;and population growth and drinking water exert a positive effect on environmental sustainability in both the short-and long-run. The results of Dumitrescu Hurlin causality test indicate that environmental sustainability is unidirectionally affected by economic growth. Based on these outcomes, we suggest the following policies:(1) the EU and OECD should implement member-targeted policies on economic growth and fossil-fuel use towards regulating industrial pollution, water use, and population control;and(2) the EU and OECD member countries should invest in environmental technologies through green research and development(R&D) to transform their dirty industrial processes and ensure productive energy use.

A Two Sample Test based on U-statistic for Functional Data

We propose a two-sample test for the mean functions of functional data when the number of bases is much lager than the sample size.The novel test is based on U-statistics which avoids estimating the covariance operator accurately under the high dimensional situation.We further prove the asymptotic normality of our test statistic under both null hypothesis and a local alternative hypothesis.An extensive simulation study is presented which shows that the proposed test works well in comparison with several other methods under the high dimensional situation.An application to egg-laying trajectories of Mediterranean fruit flies data set demonstrates the applicability of the method.

High-dimensional Tests for Mean Vector: Approaches without Estimating the Mean Vector Directly

Several tests for multivariate mean vector have been proposed in the recent literature.Generally,these tests are directly concerned with the mean vector of a high-dimensional distribution.The paper presents two new test procedures for testing mean vector in large dimension and small samples.We do not focus on the mean vector directly,which is a different framework from the existing choices.The first test procedure is based on the asymptotic distribution of the test statistic,where the dimension increases with the sample size.The second test procedure is based on the permutation distribution of the test statistic,where the sample size is fixed and the dimension grows to infinity.Simulations are carried out to examine the finite-sample performance of the tests and to compare them with some popular nonparametric tests available in the literature.

Sign-based Test for Mean Vector in High-dimensional and Sparse Settings

In this article, we introduce a robust sparse test statistic which is based on the maximum type statistic. Both the limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives. Numerical studies are carried out to examine the numerical performance of the test and to compare it with other tests available in the literature. The numerical results show that the test proposed significantly outperforms those tests in a range of settings, especially for sparse alternatives.