Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data

Given the confirmed effectiveness of the survey‐based consumer sentiment index (CSI) as a leading indicator of real economic conditions, the CSI is actively used in making policy judgments and decisions in many countries. However, although the CSI offers qualitative information for presenting current conditions and predicting a household's future economic activity, the survey‐based method has several limitations. In this context, we extracted sentiment information from online economic news articles and demonstrated that the Korean cases are a good illustration of applying a text mining technique when generating a CSI using sentiment analysis. By applying a simple sentiment analysis based on the lexicon approach, this paper confirmed that news articles can be an effective source for generating an economic indicator in Korea. Even though cross‐national comparative research results are suited better than national‐level data to generalize and verify the method used in this study, international comparisons are quite challenging to draw due to the necessary linguistic preprocessing. We hope to encourage further cross‐national comparative research to apply the approach proposed in this study.     

Robustness of alternative non‐linearity tests for SETAR models

In recent years there has been a growing interest in exploiting potential forecast gains from the non‐linear structure of self‐exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR‐type non‐linearities in an observed time series. It is important to study the power and robustness properties of these tests since erroneous test results might lead to misspecified prediction problems. In this paper we investigate the robustness properties of several commonly used non‐linearity tests. Both the robustness with respect to outlying observations and the robustness with respect to model specification are considered. The power comparison of these testing procedures is carried out using Monte Carlo simulation. The results indicate that all of the existing tests are not robust to outliers and model misspecification. Finally, an empirical application applies the statistical tests to stock market returns of the four little dragons (Hong Kong, South Korea, Singapore and Taiwan) in East Asia. The non‐linearity tests fail to provide consistent conclusions most of the time. The results in this article stress the need for a more robust test for SETAR‐type non‐linearity in time series analysis and forecasting. Copyright © 2004 John Wiley & Sons, Ltd.     

Study on Approach for Computer-Aided Design and Machining of General Cylindrical Cam Using Relative Velocity and Inverse Kinematics

Cylindrical Cam Mechanism which is one of the best eq uipments to accomplish an accurate motion transmission is widely used in the fie lds of industries, such as machine tool exchangers, textile machinery and automa tic transfer equipments. This paper proposes a new approach for the shape design and manufacturing of the cylindrical cam. The design approach uses the relative velocity concept and the manufacturing approach uses the inverse kinematics concept. For the shape desig n, the contact points betw...     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

The phosphoinositide PI(3,5)P2 mediates activation of mammalian but not plant TPC proteins: functional expression of endolysosomal channels in yeast and plant cells

Two-pore channel proteins (TPC) encode intracellular ion channels in both animals and plants. In mammalian cells, the two isoforms (TPC1 and TPC2) localize to the endo-lysosomal compartment, whereas the plant TPC1 protein is targeted to the membrane surrounding the large lytic vacuole. Although it is well established that plant TPC1 channels activate in a voltage- and calcium-dependent manner in vitro, there is still debate on their activation under physiological conditions. Likewise, the mode of animal TPC activation is heavily disputed between two camps favoring as activator either nicotinic acid adenine dinucleotide phosphate (NAADP) or the phosphoinositide PI(3,5)P₂. Here, we investigated TPC current responses to either of these second messengers by whole-vacuole patch-clamp experiments on isolated vacuoles of Arabidopsis thaliana. After expression in mesophyll protoplasts from Arabidopsis tpc1 knock-out plants, we detected the Arabidopsis TPC1-EGFP and human TPC2-EGFP fusion proteins at the membrane of the large central vacuole. Bath (cytosolic) application of either NAADP or PI(3,5)P₂ did not affect the voltage- and calcium-dependent characteristics of AtTPC1-EGFP. By contrast, PI(3,5)P₂ elicited large sodium currents in hTPC2-EGFP-containing vacuoles, while NAADP had no such effect. Analogous results were obtained when PI(3,5)P₂ was applied to hTPC2 expressed in baker’s yeast giant vacuoles. Our results underscore the fundamental differences in the mode of current activation and ion selectivity between animal and plant TPC proteins and corroborate the PI(3,5)P₂-mediated activation and Na⁺ selectivity of mammalian TPC2.     

Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes

We introduce a new strategy for the prediction of linear temporal aggregates; we call it ‘hybrid’ and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction with data and models aggregated according to the forecasting horizon of interest. We develop explicit expressions that approximately quantify the mean square forecasting errors associated with the different prediction schemes and that take into account the estimation error component. These approximate estimates indicate that the hybrid forecasting scheme tends to outperform the so‐called ‘all‐aggregated’ approach and, in some instances, the ‘all‐disaggregated’ strategy that is known to be optimal when model selection and estimation errors are neglected. Unlike other related approximate formulas existing in the literature, those proposed in this paper are totally explicit and require neither assumptions on the second‐order stationarity of the sample nor Monte Carlo simulations for their evaluation. Copyright © 2014 John Wiley & Sons, Ltd.     

Antiinflammatory and analgesic effect of herbal cocktail Hongbaekjeong via inhibition of proinflammatory cytokines and prostaglandin E2 release

Though Hongbaekjeong(HBJ),a hebal mixture of three medicinal plants,has been traditionally used for arthritis and muscular pain,its scientific evidence still remains unclear.Thus,in the present study,analgesic and anti-inflammatory mechanism of HBJ was evaluated in vitro and in vivo.HBJ significantly reduced NO production and prostaglandin E2(PGE2)release and also attenuated the expression of cyclooxygenase 2(COX-2)in lipopolysaccharides(LPS)and interferon(IFN)-c treated RAW 264.7 cells.Furthermore,HBJ abrogated the production of proinflammatory cytokines such as interleukin(IL)1b,IL-6,IL-8 and monocyte chemoattractant protein-1(MCP-1)in LPS and IFN-c treated RAW 264.7 cells.In addition,HBJ significantly decreased the number of writhing syndrome induced by acetic acid,and also increased latency in hot-plate method and tail flick test in mice.Consistently,HBJ significantly reduced the edema volume in the hind paw of the rats with arthritis induced by Freund’s complete adjuvant(FCA)compared to untreated control.Collectively,our findings demonstrate the antiinflammatory and analgesic potential of HBJ via inhibition of proinflammatory cytokines and PGE2 release for treatment of arthritis and muscular pain.     

A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates

Observing that a sequence of negative logarithms of 1‐year survival probabilities displays a linear relationship with the sequence of corresponding terms with a time lag of a certain number of years, we propose a simple linear regression to model and forecast mortality rates. Our model assuming the linearity between two mortality sequences with a time lag each other does not need to formulate the time trends of mortality rates across ages for mortality prediction. Moreover, the parameters of our model for a given age depend on the mortality rates for that age only. Therefore, whether the span of the study ages with the age included is widened or shortened will not affect the results of mortality fitting and forecasting for that age. In the empirical testing, the regression results using the mortality data for the UK, USA and Japan show a satisfactory goodness of fit, which convinces us of the appropriateness of the linear assumption. Empirical illustrations further show that our model's performances of fitting and forecasting mortality rates are quite satisfactory compared with the existing well‐known mortality models. Copyright © 2015 John Wiley & Sons, Ltd.     

WTI crude oil option implied VaR and CVaR: An empirical application

Using option market data we derive naturally forward‐looking, nonparametric and model‐free risk estimates, three desired characteristics hardly obtainable using historical returns. The option‐implied measures are only based on the first derivative of the option price with respect to the strike price, bypassing the difficult task of estimating the tail of the return distribution. We estimate and backtest the 1%, 2.5%, and 5% WTI crude oil futures option‐implied value at risk and conditional value at risk for the turbulent years 2011–2016 and for both tails of the distribution. Compared with risk estimations based on the filtered historical simulation methodology, our results show that the option‐implied risk metrics are valid alternatives to the statistically based historical models.