Design of robotic arm for the porcelain bushing in substation.

With the development and the application popularization of artificial intelligence robot technology and 5G technology, a robotic arm is designed and developed for rinsing porcelain bushing in high voltage substation in this paper. Firstly, the components and implementation of robotic arm are presented, subsequently, a circular cleaning structure with a 120-degree split is proposed to rinse the porcelain bushing. Secondly, a two-stage simple and effective method to realize automatic orientation is proposed utilizing photoelectric switches. Moreover, a prototype of robotic arm with control system is developed based on the regime switching function, and the result of edge computing is transmitted by 5G technology. Finally, feasibility and effectiveness of the robotic arm are verified in the Nanjing power grid. The case study manifests that the robotic arm developed by the proposed method in the paper can achieve efficient rinsing and all the corresponding information can be transmitted preciously. The proposed method lays a foundation for wide application of cleaning robot in high voltage substation.

Survival analysis and probability density function of switching heroin model.

We study a switching heroin epidemic model in this paper, in which the switching of supply of heroin occurs due to the flowering period and fruiting period of opium poppy plants. Precisely, we give three equations to represent the dynamics of the susceptible, the dynamics of the untreated drug addicts and the dynamics of the drug addicts under treatment, respectively, within a local population, and the coefficients of each equation are functions of Markov chains taking values in a finite state space. The first concern is to prove the existence and uniqueness of a global positive solution to the switching model. Then, the survival dynamics including the extinction and persistence of the untreated drug addicts under some moderate conditions are derived. The corresponding numerical simulations reveal that the densities of sample paths depend on regime switching, and larger intensities of the white noises yield earlier times for extinction of the untreated drug addicts. Especially, when the switching model degenerates to the constant model, we show the existence of the positive equilibrium point under moderate conditions, and we give the expression of the probability density function around the positive equilibrium point.

Determinants of the European Sovereign Debt Crisis: Application of Logit, Panel Markov Regime Switching Model and Self Organizing Maps.

The study aims to empirically identify the determinants of the debt crisis that occurred within the framework of 15 core EU member countries (EU-15). Contrary to previous empirical studies that tend to use event-based crisis indicators, our study develops a continuous fiscal stress index to identify the debt crises in the EU-15 and employs three different estimation techniques, namely self-organizing map, multivariate logit and panel Markov regime switching models. Our estimation results show first that the study correctly identifies the time and the length of the debt crisis in each EU-15-member country. Empirical results then indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Furthermore, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15.

The oil price-macroeconomy dependence.

This paper investigates the relationship between the price of oil and real output in the United States in the context of a Markov regime switching, identified, structural GARCH-in-Mean VAR model with copulas. We use the copula method to investigate the nonlinear dependence structure, as well as (upper and lower) tail dependence, between the price of oil and real output growth, and Markov regime switching to account for changing oil price dynamics over the sample period. We find an asymmetric negative dependence structure between oil price and output growth shocks and that oil price uncertainty has a negative and statistically significant effect on real output growth.

An impact assessment of the COVID-19 pandemic on Japanese and US hotel stocks.

This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US, taking into account the role of stock markets. The first model is a direct impact model of COVID-19 on hotel stock prices; the analysis finds that infection speed negatively affects Japanese hotel stock prices and shows that the regime continues to switch to high volatility in prices due to COVID-19 until September 2021, unlike US stock prices. The second model is a hybrid model with COVID-19 and stock market impacts on the hotel stock prices, which can remove the market impacts on regime-switching volatility; this analysis demonstrates that COVID-19 negatively affects hotel stock prices regardless of whether they are in Japan or the US. We also observe a transition to a high-volatility regime in hotel stock prices due to COVID-19 until around summer 2021 in both Japan and the US. These results suggest that COVID-19 is likely to affect hotel stock prices in general, except for the influence of the stock market. Considering the market influence, COVID-19 directly and/or indirectly affects Japanese hotel stocks through the Japanese stock market, and US hotel stocks have limited impacts from COVID-19 owing to the offset between the influence on hotel stocks and no effect on the stock market. Based on the results, investors and portfolio managers should be aware that the impact of COVID-19 on hotel stock returns depends on the balance between the direct and indirect effects, and varies from country to country and region to region.

COVID-19 and stock returns: Evidence from the Markov switching dependence approach.

This paper aims to investigate the regime-switching and time-varying dependence between the COVID-19 pandemic and the US stock markets using a Markov-switching framework. It makes two contributions to the empirical literature by showing that: (a) the variations of the daily reported COVID-19 cases and cumulative COVID-19 deaths induced asymmetric lower (left) and upper (right) tail dependence with the stock markets, and its left and right tail dependence exhibited significant time-varying trends; and (b) the left and right tail dependence between the stock markets and the pandemic exhibited significant regime-switching behaviours, with its switching probabilities in the higher tail dependence stage all being greater than in the lower tail dependence stage after 1 December 2019. Moreover, given that there is concurrent but significant financial market reaction to any unexpected emergence of a transmittable respirational disease or a natural calamity, the outcomes have some vital implications to market players and policymakers.

Dynamical behavior of a stochastic SIQS model via isolation with regime-switching.

A stochastic SIQS model via isolation with regime-switching is studied in this paper. The range of positive solution of the model is presented. Threshold to determine extinction and invariant measure is obtained by a new technique, which can be seen as the sufficient and almost necessary condition. Meantime, a value to judge the existence of stationary distribution is acquired by constructing the suitable hybrid Lyapunov function. Two values are proved to be consistent. Several examples are enumerated to test the theoretical results.

World pandemic uncertainty and German stock market: evidence from Markov regime-switching and Fourier based approaches.

This study aims to examine the impact of the world pandemic uncertainty index on the German stock market index (DAX index) for the 1996Q1 to 2020Q3 period while controlling real effective exchange rate, industrial production index, and consumer price index. The present study performs the Fourier Augmented Dickey-Fulle Unit Root, Fourier Engle-Granger Cointegration, Bayer-Hanck Cointegration, and Markov switching regression tests. The outcomes disclose that there is a long-run cointegration association between the stock market index and world pandemic uncertainty index, real effective exchange rate, industrial production index, and consumer price index in Germany, indicating that the combination of these factors significantly affects the German stock market index in the long-run. Moreover, in both high and low volatile regimes, the world pandemic uncertainty index and real effective exchange rate negatively affect the German stock market index while industrial production and consumer price indices impact positively.

Constrained portfolio strategies in a regime-switching economy.

We implement an allocation strategy through a regime-switching model using recursive utility preferences in an out-of-sample exercise accounting for transaction costs. We study portfolios turnover and leverage, proposing two procedures to constrain the allocation strategies: a low-turnover control (LoT) and a maximum leverage control (MaxLev). LoT sets a dynamic threshold to trim minor rebalancing, reducing portfolio turnover, mitigating costs. MaxLev calculates dynamic adjustments to the risk aversion parameter to constrain the portfolio leverage. The MaxLev adjustments depend on the risk aversion and permitted portfolio leverage, which enables optimal strategies considering the leverage constraints. The study uses US equity portfolios, and shows that, first, models with LoT result in superior return-to-risk measures than those without it when transaction costs increase. Second, considering transaction costs, the return-to-risk measures of the models using MaxLev closely match or exceed those from the corresponding unconstrained regime-switching benchmarks. Third, MaxLev returns have lower volatility and higher return-to-risk than conventional numerically constrained benchmarks. Fourth, the certainty equivalent returns indicate that models using MaxLev and LoT outperform both single-state models and unconstrained regime-switching models with statistical significance. Supplementary Information: The online version contains supplementary material available at 10.1007/s11408-022-00414-x.

A regime-switching skew-normal model of contagion in some selected stock markets.

This study examined the contagion and structural break between Nigerian Stock Exchange Market (NSE) and some selected stock markets, namely: Ghana, South Africa (SA), Tunisia, and the United States. Two periods were considered: the crisis period (1st May 2016 to 31st December 2017) and the calm period (1st January 2018 to 31st December 2019). Following the work of (Chan, J., Fry-McKibbin, R. & Hsiao C. (2018). A Regime switching skew-normal model of contagion. Studies in Nonlinear Dynamics and Econometrics, Volume 23, Issue 1), the study used the Regime Switching Skew-Normal (RSSN) model which is capable of measuring contagion and structural breaks between markets. Our results indicated evidence of a structural break between the crisis and calm periods, which is a prerequisite for contagion. Furthermore, the study found a moderate contagion between Nigeria and SA stock markets but an absence of contagion between Nigeria and the remaining stock markets, suggesting capital flights from Nigeria to SA for safety during the 2016 economic recession. However, we were unable to find any evidence of capital reversal to Nigeria from SA during the calm period, implying an existence of an asymmetric relationship between Nigeria and South African stock markets. The absence of contagion between Nigeria and the selected African stock markets, suggests there is no significant economic cooperation and cross-border portfolio investment flow among the countries. This development further underpins the imperativeness of the full implementation of the African Continental Free Trade Agreement (AfCFTA), which encourages economic activities and investment flow on the continent.

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.

Spatio-temporal models need to address specific features of spatio-temporal infection data, such as periods of stable infection levels (endemicity), followed by epidemic phases, as well as infection spread from neighbouring areas. In this paper, we consider a mixture-link model for infection counts that allows alternation between epidemic phases (possibly multiple) and stable endemicity, with higher AR1 coefficients in epidemic phases. This is a form of regime-switching, allowing for non-stationarity in infection levels. We adopt a generalised Poisson model appropriate to the infection count data and avoid transformations (e.g., differencing) to alternative metrics, which have been adopted in many studies. We allow for neighbourhood spillover in infection, which is also governed by adaptive regime-switching. Compared to existing models, the observational (in-sample) model is expected to better reflect the balance between epidemic and endemic tendencies, and short-term extrapolations are likely to be improved. Two case study applications involve COVID area-time data, one for 32 London boroughs (and 96 weeks) since the start of the COVID epidemic, the other for a shorter time span focusing on the epidemic phase in 144 areas of Southeast England associated with the Alpha variant. In both applications, the proposed methods produce a better in-sample fit and out-of-sample short term predictions. The spatial dynamic implications are highlighted in the case studies.

Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models.

This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In this study, we performed time series forecasting for the created polarity index using deep learning, and clustered the forecasted values by volatility using a regime switching model. In addition, we constructed a portfolio from stock data and rebalanced it at each change point of the regime. Consequently, the investment strategy proposed in this study outperforms the benchmark portfolio in terms of returns. This suggests that the polarity index is useful for constructing stock investment strategies.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

A stochastic mussel-algae model under regime switching.

We investigate a novel model of coupled stochastic differential equations modeling the interaction of mussel and algae in a random environment, in which combined effect of white noises and telegraph noises formulated under regime switching are incorporated. We derive sufficient condition of extinction for mussel species. Then with the help of stochastic Lyapunov functions, a well-grounded understanding of the existence of ergodic stationary distribution is obtained. Meticulous numerical examples are also employed to visualize our theoretical results in detail. Our analytical results indicate that dynamic behaviors of the stochastic mussel-algae model are intimately associated with two kinds of random perturbations.

Regime-switching empirical similarity model: a comparison with baseline models.

In this paper, I extend the standard specification of the empirical similarity (ES) model of Gilboa et al. (Rev Econ Stat 88:433-444, 2006) to account for changes in parameters. I implement this by allowing for a combination of component ES models in the spirit of Gaussian mixture models. The predictive power of the modified model, along with that of the standard specification, will be assessed and compared to the baseline models consisting of autoregressions and Markov-switching autoregressions within a simulation exercise. Finally, we also compare the predictive ability of models using data on quarterly US real GDP growth. The results indicate that in situations of a more complex regime-switching behavior and a moderate to high autocorrelation in series, modified ES model demonstrates a better empirical fit. In addition, results of the empirical example show that modified ES models can better predict more extreme observations.

Dynamic behavioral characteristics of carbon dioxide emissions from energy consumption: the role of shale technology.

Dynamic behavioral analysis of carbon dioxide ([Formula: see text]) emissions to moderate the climate change helps to upgrade the developing measures utilized throughout the energy system decarbonization and mitigate global warming. Therefore, this research aims to analyze the role of the shale gas technology in behavioral characteristics of the US energy-related [Formula: see text] emissions. To this end, first, the Markov regime-switching methodology is used to assess the scale- and technology effects of the shale revolution on the switching-regimes for source-/sector-based [Formula: see text] emissions cycles of the US economy. Then, the dynamic network connectedness measures are utilized to determine the changes in the spillover effects between [Formula: see text] emissions cycle series by source/sector pre- and post-shale revolution. The findings indicate asymmetric and time-varying behavior of [Formula: see text] emissions cycles pre- and post-revolution. Particularly, the greater total spillover effect of the US source- and sector-based [Formula: see text] emissions network is accompanied with the higher speed of "downward" regime following the revolution that lowers environmental degradation of the US economy. Hence, utilization of the US economies of scale in the shale technology develops the coordinating mechanism, which can support the cooperative relationship between sources/sectors of the energy system in response to the risks, time and cost change, caused by the shale revolution.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Export quality, economic growth, and renewable-nonrenewable energy use: non-linear evidence through regime shifts.

Since export has a key role in economic growth in terms of national production quantity, export quality can be considered another important factor regarding the revenue from the export product. Hence, both export and export quality can contribute to the economic growth process positively when the countries' terms of trade have moved in a favorable direction from this point of view, it is essential to examine the relationship between the energy-growth nexus and export quality. Although available seminal studies are monitoring the energy-growth nexus, there exists a limited number of works employing the export quality. Besides, one might claim that there exists no research on how the terms of trade (export quality) alter the economic growth and energy use through regime shifts. Markov regime-shifting models estimate (a) the impact of export and terms of trade on growth, and (b) the effect of growth on the use of fossil energy and renewable energy for the USA at regime 1 and regime 2 for the period 1980:Q4-2019:Q2. After conducting the non-linear analyses, this paper (i) reveals the estimated parameters varying from one regime to another regime through transition probabilities, (ii) finds evidence that (a) export and export quality growths affect positively GDP growth, (b) GDP growth increases fossil fuel consumption growth, (c) renewable energy growth increases at decreasing rate due to GDP growth, and (iii) yields relevant energy-environmental policy proposals by underlying the prominence of terms of trade within growth-energy nexus.

Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model.

Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.

Timing differences in the impact of Covid-19 on price volatility between assets.

We empirically examine the impacts of Covid-19 on asset price volatilities by focusing on the timing. This paper has three contributions. First, we propose a new Covid-19 dependent regime-switching volatility model for the examination. Second, results show a shift to a higher price volatility regime from a lower one for financial assets and commodities after late February 2020 when Covid-19 spread all over the world, but the timing of the impacts varies from immediate timing for the S&P 500, the FTSE 100, the COMEX gold and silver futures to the delayed timing for the ICE Brent crude oil futures followed by the timing for the ICE UK natural gas futures. Third, we find the sensitivity of Covid-19 information to the regime switch differs between financial assets and precious metal ones which have the immediate impacts: the infection speed, i.e. the changes in the number of Covid-19 infected individuals, enhance the impacts on the tendency to a high price volatility regime for the S&P 500 and the FTSE 100; both the infection speed and the number of the deaths mitigate those impacts for the gold and silver futures, respectively during a turmoil period due to Covid-19, suggesting that the gold and silver markets are functioning as risk-hedging safety assets alternative to financial assets during Covid-19 turmoil.