We apply a Quantile unit root test with both Sharp Shifts and Smooth Breaks to revisit hysteresis in unemployment for G7 countries using data for the period 1980-2017. Results from the conventional unit root tests indicate that hysteresis in unemployment does hold in half of these G7 countries during the period 1980-2017. A quantile Kolmogorov-Smirnov test fails to reject hysteresis in the unemployment hypothesis for our quarterly data but not in monthly data in G7 countries. Empirical results from our proposed quantile unit root test considering both sharp shifts and smooth breaks indicate that hysteresis in unemployment can be rejected over certain quantiles. A quantile Kolmogorov-Smirnov test results demonstrating hysteresis in unemployment does not hold in G7 countries for both monthly and quarterly data. These empirical findings have important policy implications in G7 countries.
Further investigation of the methanolic extract of Fissistigma latifolium resulted in two new compounds whose structures were assigned as 2,5,6,7-tetramethoxyflavan (1) and 2′-hydroxy-4′,5′,6′-trimethoxybenzil (2). These two compounds were determined on the basis of chemical and spectroscopic evidences. Compound 2 is the first report of benzil from Fissistigma species. 2,5,6,7-Tetramethoxyflavan (1) showed a potent inhibitory effect on superoxide anion production in formyl-L-methionyl-L-leucyl-L-phenylalanine (fMLP)/cytochalasin B (CB)-activated human neutrophils.
This paper presents minimum mean square error (MMSE) estimators for mean life and failure rate of Exponential distribution model based on failure censored step-stress accelerated lifetesting (SSALT) data. The MMSE estimators are drived by revising the corresponding unbiased estimators in terms of mean square error (MSE). Two theorems prove mathematically the fact that MSE of the resulting MMSE estimators are smaller than that of the corresponding unbiased estimators. The results show that the MMSE estimators are more efficient than the unbiased estimators and maximum likelihood estimators (MLEs) in small and moderate sample size.
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