Time-varying Parameters Research Articles

Partisan conflict, trade policy uncertainty, and the energy market

This paper investigates the time-varying effects of partisan conflict (PC) and trade policy uncertainty (TPU) on the energy market from January 2000 to July 2021 by employing a time-varying parameter vector autoregressive model with stochastic volatility. The results show that the pass-through effects of the PC and TPU shocks on energy return and volatility varied over time and are different across energy sub-sectors. PC and TPU had a greater impact on energy market return in the short-term, whereas the impact on energy market volatility was spread across relatively long periods. Generally, the impact of TPU on energy return and volatility was greater than that of PC at different lag periods and different points. Further, under different energy frameworks, the trend of the time-varying relationship between PC and TPU was roughly the same, indicating the robustness of our results.

Quantifying the effectiveness of brucellosis control strategies in northern China using a mechanistic and data-driven model

In recent years, with the frequent movement of livestock and related products on the Chinese mainland that have resulted in a rise in brucellosis cases in certain regions, the Chinese government has implemented “vaccination + culling” strategy to control the spread of brucellosis, but the epidemiological situation and the intensity of prevention and control measures vary from province to province, and much of the situation has not yet been reversed. Thus, it is crucial to understand and assess the combined effects of vaccination and culling on brucellosis transmission behind the data from multiple provinces and cities. In this paper, we combine an ordinary differential equation model and a deep neural network model to develop a hybrid model that not only characterizes the propagation mechanism of brucellosis, but also describes control strategies as time-varying parameters to track the dynamic evolution of brucellosis transmission under control measures with varying intensity. In addition, the real-time reproduction number is calculated and taken as an evaluation index. We then train and test the hybrid model using actual data from four provincial administrative regions in Northern China from 2010 to 2020. It is found that in Inner Mongolia, the sheep vaccination rate (θ(t)) and the culling rate of infected sheep (c(t)) have a weak effect on reducing the brucellosis transmission. In Gansu, the incomplete culling of infected sheep was the main reason for the increase in human brucellosis cases after the introduction of whole-flock vaccination in 2016. Culling and vaccination contributed to the decrease in human cases in both Xinjiang and Jilin. Prevention and control measures for human brucellosis are inadequate in Xinjiang. However, prevention and control measures for human brucellosis are inadequate, while indirect transmission of Brucella from the environment to humans and sheep poses a greater risk than direct contact with infected sheep in Jilin. The above analysis can provide theoretical foundation for decision-making on public health policies to control the pandemic.

Nonnegative GARCH-type models with conditional Gamma distributions and their applications

Most of real data are characterized by positive, asymmetric and skewed distributions of various shapes. Modelling and forecasting of such data are addressed by proposing nonnegative conditional heteroscedastic time series models with Gamma distributions. Three types of time-varying parameters of Gamma distributions are adopted to construct the nonnegative GARCH models. A condition for the existence of a stationary Gamma-GARCH model is given. Parameter estimates are discussed via maximum likelihood estimation (MLE) method. A Monte-Carlo study is conducted to illustrate sample paths of the proposed models and to see finite-sample validity of the MLEs, as well as to evaluate model diagnostics using standardized Pearson residuals. Furthermore, out-of-sample forecasting analysis is performed to compute forecasting accuracy measures. Applications to oil price and Bitcoin data are given, respectively.

Time-varying interrelations between digitalization and human capital in Vietnam

In this study, we use a time-varying parameter vector autoregression in conjunction with the extended joint connectedness approach (TVPVAR-EJC) to examine the interrelationships among five variables, namely digitalization measured by the number of individuals using the Internet (% of the population), mobile cellular subscriptions (per 100 people), human capital index, CO2 emissions, and GDP, from 1996 to 2019 in Vietnam. Net total directional connectedness of digitalization and other variables suggests that it consistently acts as a net shock transmitter, whereas human capital was found to be a net receiver of corresponding shocks from 1997 to 2019. The emission of CO2 was a net critical receiver in 1998 but remained as a net transmitter for the rest of the period. Pairwise connectedness reveals that digitalization appeared as a net shock transmitter in connection with the human capital index and GDP. In the relationship with human capital, digitalization’s role was critical.

Fair Resource Allocation in Virtualized O-RAN Platforms

O-RAN systems in virtualized platforms (O-Cloud) offer performance boosts but also raise energy concerns. This paper assesses O-Cloud's energy costs and proposes energy-efficient policies for base station (BS) data loads and transport block (TB) sizes. These policies balance energy savings and performance fairly across servers and users. To handle the unknown and time-varying parameters affecting the policies, we develop a novel online learning framework with fairness guarantees that apply to the entire operation horizon of the system (long-term fairness).

Bridging the Covid-19 data and the epidemiological model using the time-varying parameter SIRD model

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases’ effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic’s current stance. This superior performance, in turn, transforms into accurate predictions of the death cases and cases treated in Intensive Care Units (ICUs).

Dynamic Robust Optimization Method Based on Two-Stage Evaluation and Its Application in Optimal Scheduling of Integrated Energy System

As an emerging energy allocation method, shared energy storage devices play an important role in modern power systems. At the same time, with the continuous improvement in renewable energy penetration, modern power systems are facing more uncertainties from the source side. Therefore, a robust optimization algorithm that considers both shared energy storage devices and source-side uncertainty is needed. Responding to the above issues, this paper first establishes an optimal model of a regional integrated energy system with shared energy storage. Secondly, the uncertainty problem is transformed into a dynamic optimization problem with time-varying parameters, and a modified robust optimization over time algorithm combined with scenario analysis is proposed to solve such optimization problems. Finally, an optimal scheduling objective function with the lowest operating cost of the system as the optimization objective is established. In the experimental part, this paper first establishes a dynamic benchmark test function to verify the validity of proposed method. Secondly, the multi-mode actual verification of the proposed algorithm is carried out through a regional integrated energy system. The simulation results show that the modified robust optimization over time (ROOT) algorithm could find solutions with better robustness in the same dynamic environment based on the two-stage evaluation strategy. Compared with the existing algorithms, the average fitness and survival time of the robust solution obtained by the modified ROOT algorithm are increased by 94.41% and 179.78%. At the same time, the operating cost of the system is reduced by 11.65% by using the combined optimization scheduling method proposed in this paper.

Stochastic occupancy modeling for spaces with irregular occupancy patterns using adaptive B-Spline-based inhomogeneous Markov Chains

This paper presents a discrete time, discrete state-space in-homogeneous Markov Chains model for stochastic occupancy modeling in spaces with irregular occupancy patterns. The goal of the model is to provide accurate predictions of occupancy numbers, enabling appropriate actions to be taken for HVAC system to maintain optimal indoor environment. The proposed Markov Chain model incorporates time in-homogeneity by coupling the time-varying model parameters using a Periodic B-Spline expansion with adaptive knots, which effectively captures patterns in occupancy activity. This method optimizes the distribution of knots based on specific occupancy characteristics observed in different types of rooms. To evaluate the effectiveness of the proposed method, six months of occupancy data collected from a meeting room are utilized. A comprehensive comparison is conducted between the proposed adaptive B-Spline method and other approaches, including the counting method and uniform B-Spline method. The comparison considers both model accuracy and complexity, using metrics such as the Akaike Information Criterion and Bayesian Information Criterion. Results indicate that the proposed model achieves more accurate predictions with fewer model parameters compared to other methods. These forecasts are particularly useful in optimizing the control of HVAC systems, where accurate predictions of future occupancy numbers are essential.

Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic Control

Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic Control

Herding behaviour surrounding the Russo–Ukraine war and COVID-19 pandemic: evidence from energy, metal, livestock and grain commodities

PurposeThis study examines herding behaviour in commodity markets amid two major global upheavals: the Russo–Ukraine conflict and the COVID-19 pandemic.Design/methodology/approachBy analysing 18 commodity futures worldwide, the study examines herding trends in metals, livestock, energy and grains sectors. The applied methodology combines static and dynamic approaches by incorporating cross-sectional absolute deviations (CSAD) and a time-varying parameter (TVP) regression model extended by Markov Chain Monte Carlo (MCMC) sampling to adequately reflect the complexity of herding behaviour in different market scenarios.FindingsOur results show clear differences in herd behaviour during these crises. The Russia–Ukraine war led to relatively subdued herding behaviour in commodities, suggesting a limited impact of geopolitical turmoil on collective market behaviour. In stark contrast, the outbreak of the COVID-19 pandemic significantly amplified herding behaviour, particularly in the energy and livestock sectors.Originality/valueThis discrepancy emphasises the different impact of a health crisis versus a geopolitical conflict on market dynamics. This study makes an important contribution to the existing literature as it is one of the first studies to contrast herding behaviour in commodity markets during these two crises. Our results show that not all crises produce comparable market reactions, which underlines the importance of the crisis context when analysing financial market behaviour.

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Asymmetric Return Connectedness Among Indian Equity Sectors: Insights from Recent Global Disruptions

This article aims to study asymmetric shock transmission among India’s National Stock Exchange sectoral indexes before and amid the COVID-19 crisis and the Russian–Ukraine feud using an asymmetric time-varying parameter vector autoregressive connectedness technique. The daily closing price of equity sector indexes from 1 January 2018 to 31 March 2023 were used. The research reveals strong sectoral return interconnectedness, with negative returns spillover dominating the assessment period. COVID-19 substantially impacted connectedness more than the Russian–Ukraine feud. The sectors such as bank, private bank and financial services were net transmitters of risk, and information technology was the net recipient of risk throughout the assessment period, indicating these sectors are not much affected by these crises. Sector return spillover patterns were similar during the epidemic and the Russian–Ukraine dispute, except for the oil and gas and metal sectors, which shifted roles. These findings suggest that policymakers must recognize and assess the peculiarities of industries that are high-risk transmitters when formulating strategies to prevent market failure or mitigate risk transmission to others. Investors may benefit from recognizing industries that operate as risk transmitters and receivers to create effective risk-mitigation investment strategies amid turbulent times.

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests

Objective. The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake ( V˙O2 ) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linear V˙O2 responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR and V˙O2 data of single ramp incremental running tests. Approach. A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate. Main results. As the work rate increases, τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s for V˙O2 . Both increases are statistically significant (p < 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h−1)−1 for HR, and from 218 to 144 ml·min−1 for V˙O2 (p < 0.01 for decrease parameters of HR and V˙O2 ). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures. Significance. We show that time-variant modeling is able to determine the time-varying dynamics HR and V˙O2 responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Recursive Filtering Under Probabilistic Encoding-Decoding Schemes: Handling Randomly Occurring Measurement Outliers.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

Observational constraints on the wet dark fluid model in the fractal gravity

A flat Friedmann–Robertson–Walker (FRW) cosmological model with Wet Dark Fluid has been studied based on fractal gravity. The exact solution of the field equations is obtained using linear time-varying deceleration parameter (Akars̈u and Dereli, 2012) and assuming fractal parameter ξ≠0. The model parameters involved in the model have been constrained using the Hubble datasets H(z) of 77 data points, known as cosmic chronometers (CC), recently published Pantheon, and the joint data (CC+Pantheon samples). Additionally, we compare our model with ΛCDM in standard cosmology via error bar trajectories. We study the physical and the cosmographic parameters, such as the Hubble parameter H(z), deceleration parameter q(z), and the higher derivatives of the deceleration parameter q(z), etc. under cosmic observations. It is observed that our model transits from a decelerating state to an accelerating state at ztr≈2.764. The other astrophysical parameters such as the jerk parameter and snap parameter are also discussed. Finally, we conclude that our model is an accelerating quintessence dark energy model.

Backstepping control based on adaptive neural network and disturbance observer for reconfigurable variable stiffness actuator

Reconfigurable variable stiffness actuator (RVSA) has attracted increasing attention in robotics due to its safety, compliance, and robustness. However, the control of the RVSA is challenging due to nonlinear factors such as high-order nonlinear dynamic, model uncertainties, time-varying model parameters, and disturbances. In this paper, firstly, a lightweight RVSA structure with both passive and active nonlinear variable stiffness characteristic is developed. Secondly, a dynamic surface backstepping control method based on a radial basis neural network and disturbance observer (DSBC-RBFNN-DOB) is proposed to achieve position control of the lightweight RVSA with matched and unmatched uncertainties. To address solve the “complexity explosion” and noise problems in traditional backstepping control, the dynamic surface backstepping control (DSBC) method is used to design the controller. Then, a method based on radial basis neural network (RBFNN) and disturbance observer (DOB) are used to compensate for the matched and unmatched uncertainties in the link and motor. In this method, the matched uncertainties are compensated using RBFNN, and the DOB is integrated to compensate RBFNN approximation errors and unmatched uncertainties. Through Lyapunov stability analysis, the semi-global boundedness of the controller is proven. Finally, the proposed method is simulated and actually implemented, verifying the effectiveness of the method. Simulation and experimental results show that the root mean square error (RMSE) of the proposed method is only 0.97277° and 0.6418°, respectively. Compared with PID, DSBC, and DSBC-RBFNN, the error reduction percentages in simulation (experiment) are 85.6 % (88.9 %), 49.4 % (88.4 %) and 36.1 % (80.0 %) respectively.

Monetary policy spillovers between the US and African Central Banks: A time- and frequency-varying connectedness study

This study investigates time and frequency connectedness between monetary policy in the US and 7 African countries to determine the extent to which US monetary policy influences policy conduct amongst African Central Banks. We use a time-varying parameter vector autoregressive (TVP-VAR) framework with frequencies to extract the spectral representation of the forecast error variance decompositions of the TVP-VAR and form 3 bands of frequency strata corresponding to long-run, medium-run and short-run. The results of the study are as follows: firstly, the static analysis shows that the US is the dominant transmitter of systemic shocks across all frequencies and the vulnerability of African countries as recipients of these shocks varies across frequency bands. Secondly, the dynamic analysis further reveals stronger short- and medium-run systemic connectedness during the periods of the Large Scale Asset Purchase programme and forward guidance policies whereas long-run connectedness is prominent during periods of US conventional monetary policy particularly in the COVID-19 era. The findings from the country-by-country dynamic spillover specifically show that countries which are more (less) responsive to US monetary policy shocks have lower (higher) inflation rates averages since the start of the pandemic. The findings suggest that African Central Banks can be benefit from higher coordination with the US Federal Reserve and we further propose that Central Banks worldwide in setting similar inflation targets.

On the Association Between Demographic Structural Change and the Effectiveness of Nurse Staffing Policy for Inpatient Care: Evidence from Taiwan.

This study investigates the influence of demographic changes on the effectiveness of hospital nurse staffing policy, measured by the cumulative response of inpatient care quality to adjustments in hospital nurse staffing levels in Taiwan. The research design utilized in this study aligns with the observational time-series methodology, and a total of 99 monthly time-series observations were collected from multiple databases administered by the Taiwan government over the period from January 2015 to March 2023. Specifically, the time-varying parameter vector autoregressive and autoregressive distributed lag models were employed to investigate the association between age distribution and nurse staffing policy effectiveness. The time-varying impulse responses of the unplanned 14-day readmission rate after discharge to changes in nurse staffing levels indicate a positive association between patient-to-nurse ratios and unplanned 14-day readmission rates across various types of hospitals. Nevertheless, the effectiveness of hospitals' nurse staffing policy is observed to diminish with population aging, particularly evident in medical centers and regional hospitals. Policymakers should establish lower mandated patient-to-nurse ratios, grounded in practical nurse workforce planning, to address the needs of an aging society and enhance inpatient care quality through improved nurse staffing in hospitals.

Event-Triggered Guarantee Cost Control for Partially Unknown Stochastic Systems via Explorized Integral Reinforcement Learning Strategy.

In this article, an integral reinforcement learning (IRL)-based event-triggered guarantee cost control (GCC) approach is proposed for stochastic systems which are modulated by randomly time-varying parameters. First, with the aid of the RL algorithm, the optimal GCC (OGCC) problem is converted into an optimal zero-sum game by solving a modified Hamilton-Jacobin-Isaac (HJI) equation of the auxiliary system. Moreover, in order to address the stochastic zero-sum game, we propose an on-policy IRL-based control approach involved by the multivariate probabilistic collocation method (MPCM), which can accurately predict the mean value of uncertain functions with randomly time-varying parameters. Furthermore, a novel GCC method, which combines the explorized IRL algorithm and MPCM, is designed to relax the restriction of knowing the system dynamics for the class of stochastic systems. On this foundation, for the purpose of reducing computation cost and avoiding the waste of resources, we propose an event-triggered GCC approach involved with explorized IRL and MPCM by utilizing critic-actor-disturbance neural networks (NNs). Meanwhile, the weight vectors of three NNs are updated simultaneously and aperiodically according to the designed triggering condition. The ultimate boundedness (UB) properties of the controlled systems have been proved by means of the Lyapunov theorem. Finally, the effectiveness of the developed GCC algorithms is illustrated via two simulation examples.

Underwater acoustic non-reciprocal manipulation based on dynamic-modulation structures

Abstract Underwater acoustic non-reciprocal transmission via dynamic-modulation structures with time-varying mass and stiffness is studied. The model system consists of spatiotemporally modulated discrete lattices immersed in the water background. Based on the transfer matrix method, an analytic model for the coupled continuum-discrete system is developed to calculate acoustic scattering responses in the frequency domain. Finite-difference time-domain computation is conducted for the coupled system to verify the theoretical model. Results show that acoustic non-reciprocal transmission in opposite directions appears at frequencies where there are asymmetric bandgaps in dispersion diagrams. Asymmetric transmission can be enhanced in magnitude by engineering the modulating amplitudes of time-varying parameters or increasing the number of lattice elements, while the frequency bandwidth can be broadened by cascading structural elements with different modulating frequencies due to the gap-combining effect. The model may find potential applications in underwater acoustic isolation and sonar communication.