Economic Policy Uncertainty and Conditional Dependence between China and U.S. Stock Markets

In this paper, we investigate the impact of economic policy uncertainty (EPU) on the conditional dependence between China and U.S. stock markets by employing the Copula-mixed-data sampling (Copula-MIDAS) framework. In the case of EPU, we consider the global EPU (GEPU), the American EPU (AEPU), and the China EPU (CEPU). The empirical analysis based on the Shanghai Stock Exchange Composite (SSEC) index in China and the S&P 500 index in the U.S. shows that the tail dependence between China and U.S. stock markets is symmetrical, and the t Copula outperforms alternative Copulas in terms of in-sample goodness of fit. In particular, we find that the t Copula-MIDAS model with EPU dominates the traditional time-varying t Copula in terms of in-sample fitting. Moreover, we observe that both the GEPU and AEPU have a significantly positive impact on the conditional dependence between China and U.S. stock markets, whereas CEPU has no significant impact. The tail dependence between China and U.S. stock markets exhibits an increasing trend, particularly in the recent years.

Wind Power Deterministic Prediction and Uncertainty Quantification Based on Interval Estimation

With the increasing penetration of wind power into modern power systems, accurate forecast models play a crucial role in large-scale wind power consumption and power system stability. To improve the accuracy and reliability of ultrashort-term wind power prediction, a novel deterministic prediction model and uncertainty quantification with interval estimation were proposed in this study. In consideration of the dynamic characteristics of a generator and conditional dependence, the generator rotor speed and pitch angle were regarded as the indicators of the dynamic characteristics of the generator, and light gradient boosting machine (LGBM) with a Bayesian optimization method was explored to build the deterministic prediction model. Considering the conditional dependence between output power and forecast error, A fuzzy C-means clustering method was used to cluster forecast errors into different clusters, and the best error probability distribution was obtained by fitting the error histogram with nonparametric kernel density estimation. Prediction intervals at different confidence levels were calculated and the error certainty was quantified. A case study was conducted to compare prediction accuracy and reliability by using the present and proposed methods. Results demonstrate that the LGBM deterministic prediction model combined with Bayesian optimization has better prediction accuracy and lower computational cost than the comparative models, specifically when the input features are high-dimensional big data. The nonparametric estimation method with conditional dependence is reliable for interval prediction. The proposed method has a certain reference value for wind turbines participating in frequency regulation and power control of power grid.

Correcting Frost Diagram Misconceptions Using Interactive Frost Diagrams

<p>Frost diagrams provide convenient illustrations of the aqueous reduction potentials and thermodynamic tendencies of different oxidation states of an element. Undergraduate textbooks often describe the lowest point on a Frost diagram as the most stable oxidation state of the element, but this interpretation is incorrect because the thermodynamic stability of each oxidation state depends on the specific redox conditions in solution (i.e., the potential applied by the environment or an electrode). Further confusion is caused by the widespread use of different, contradictory conventions for labeling the y-axis of these diagrams as either n<i>E</i>° or −n<i>E</i>°, among other possibilities. To aid in discussing and correcting these common mistakes, we introduce a series of interactive Frost diagrams that illustrate the conditional dependence of the relative stabilities of each oxidation state of an element. We include instructor’s notes for using these interactive diagrams and a written activity for students to complete using these diagrams.</p>

Correcting Frost Diagram Misconceptions Using Interactive Frost Diagrams

<p>Frost diagrams provide convenient illustrations of the aqueous reduction potentials and thermodynamic tendencies of different oxidation states of an element. Undergraduate textbooks often describe the lowest point on a Frost diagram as the most stable oxidation state of the element, but this interpretation is incorrect because the thermodynamic stability of each oxidation state depends on the specific redox conditions in solution (i.e., the potential applied by the environment or an electrode). Further confusion is caused by the widespread use of different, contradictory conventions for labeling the y-axis of these diagrams as either n<i>E</i>° or −n<i>E</i>°, among other possibilities. To aid in discussing and correcting these common mistakes, we introduce a series of interactive Frost diagrams that illustrate the conditional dependence of the relative stabilities of each oxidation state of an element. We include instructor’s notes for using these interactive diagrams and a written activity for students to complete using these diagrams.</p>

Evaluation using latent class models of the diagnostic performances of three ELISA tests commercialized for the serological diagnosis of Coxiella burnetii infection in domestic ruminants

ELISA methods are the diagnostic tools recommended for the serological diagnosis of Coxiella burnetii infection in ruminants but their respective diagnostic performances are difficult to assess because of the absence of a gold standard. This study focused on three commercial ELISA tests with the following objectives (1) assess their sensitivity and specificity in sheep, goats and cattle, (2) assess the between- and within-herd seroprevalence distribution in these species, accounting for diagnostic errors, and (3) estimate optimal sample sizes considering sensitivity and specificity at herd level. We comparatively tested 1413 cattle, 1474 goat and 1432 sheep serum samples collected in France. We analyzed the cross-classified test results with a hierarchical zero-inflated beta-binomial latent class model considering each herd as a population and conditional dependence as a fixed effect. Potential biases and coverage probabilities of the model were assessed by simulation. Conditional dependence for truly seropositive animals was high in all species for two of the three ELISA methods. Specificity estimates were high, ranging from 94.8% [92.1; 97.8] to 99.2% [98.5; 99.7], whereas sensitivity estimates were generally low, ranging from 39.3 [30.7; 47.0] to 90.5% [83.3; 93.8]. Between- and within-herd seroprevalence estimates varied greatly among geographic areas and herds. Overall, goats showed higher within-herd seroprevalence levels than sheep and cattle. The optimal sample size maximizing both herd sensitivity and herd specificity varied from 3 to at least 20 animals depending on the test and ruminant species. This study provides better interpretation of three widely used commercial ELISA tests and will make it possible to optimize their implementation in future studies. The methodology developed may likewise be applied to other human or animal diseases.