Dynamic Stochastic Model Research Articles

Identification-Robust Inference With Simulation-Based Pseudo-Matching

We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond the strict and exact calibration of the parameters of the simulator. Our procedure is illustrated through impulse-response (IR) matching in a simulation study of a stylized dynamic stochastic equilibrium model, and two empirical applications on the New Keynesian Phillips curve and on the Industrial Production index. In addition to usual Wald-type statistics that combine structural or reduced form IRs, we analyze local projections IRs through a factor-analytic measure of distance which eschews the need to define a weighting matrix.

Dynamic Response and Fatigue-Life Analysis of High-Speed Railway Tunnel Base with Defects

Based on the repeated “tensile-compressive-tensile-compressive” stress characteristics of tunnel-base concrete structure, double scalar damage variables were defined using the concrete stochastic damage mechanics. The elastoplastic Helmholtz free energy was corrected by introducing the hardening parameter. A stochastic dynamic damage constitutive model with the nonlinear and strain-rate effect behavior of concrete was derived via dynamic expansion of damage energy based on the principle of energy dissipation. An “elastic prediction-plastic correction” numerical analysis algorithm was developed based on the solution of the probability density evolution equation. Secondary development of the algorithm was achieved using the Universal Distinct Element Code (UDEC) for numerical calculation of the proposed constitutive model. A comparison was made between the calculation result and the result of laboratory rapid uniaxial compression test to verify the model. Dynamic response and damage features of the tunnel-base structure in three working conditions, i.e., filled layer without seam, filled layer with seam and ground water, and filled layer with seam but without water, were analyzed based on engineering practice. According to the analysis, structural vibration response was intensified in the presence of a seam. With and without groundwater, the vertical dynamic stress attenuation at both sides of the seam was 41.07% and 47.13%, respectively; vertical vibration acceleration was attenuated by 91.17% and 91.73%, respectively; and the acceleration amplitude at the upper structure of the seam increased by 724.67% and 765.02%, respectively. Groundwater in the seam would aggravate damage accumulation. It could be seen from the analysis that the current design parameters satisfied the antifatigue requirements within the design reference period at a train speed of 300 km/h when there was no seam in the tunnel-base concrete structure with IV-class surrounding rocks. When there was a seam in the tunnel-base concrete structure, however, antifatigue life was 56 years in the presence of groundwater and 62 years without groundwater, which suggested that current design parameters failed to satisfy the antifatigue requirements within the design reference period.

Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany

This paper provides an approach to incorporate planned investments in power grid infrastructure in Germany, which are expected to offer the necessary flexibility to integrate large shares of variable renewable energy sources into the power system, into a dynamic stochastic equilibrium model. Since the investments' economic impact remains unclear, our research sheds light on two questions: Do power grid infrastructure investments in Germany have the potential to positively impact economic performance, particularly GDP and employment? Is power grid infrastructure investment an efficient way to provide flexibility to the electricity system? We find the potential of negative effects of power grid infrastructure investments on economic outcomes, which can, however, be mitigated by an adequate design of the investments and its framework conditions.

On the timing and probability of Presurgical Teledermatology: how it becomes the dominant strategy.

Health level fluctuations make the outcome of any treatment option uncertain, so that decision-makers might have to wait for more information before optimally choosing treatments, especially when time spent in treatment cannot be fully recovered later in terms of health outcome. To examine whether or not, and when decision-makers should use presurgical teledermatology, a dynamic stochastic model is applied to patients waiting for dermatology surgical intervention. The theoretical model suggests that health uncertainty discourages using teledermatology. As teledermatology becomes less cost competitive, the uncertainty becomes more dominant. The results of the model were then tested empirically with the teledermatology network covering the area served by one Portuguese regional hospital, which links the primary care centers of 24 health districts with the hospital's dermatology department via the corporate intranet of the Portuguese healthcare system. Under uncertainty and irreversibility, presurgical teledermatology becomes the dominant strategy for younger patients and with lower probability of developing skin cancer.

Dynamic optimal control and simulation for unreliable manufacturing systems under perishable product and shelf life variability

This paper develops an optimal control policy for unreliable manufacturing systems evolving in a stochastic and dynamic environment and producing perishable products with limited and random shelf lives. The developed optimal control policy aims to minimize the total cost, composed of backlog, inventory holding and disposal costs. To do this, we develop a stochastic dynamic programming model and the obtained optimality conditions are solved using a numerical approach to determine the parametrized structure of the resulting optimal control policy. A simulation-based optimization approach is used to optimize the parameters of the developed control policy. This policy is subsequently analyzed using multiple sensitivity analyses and compared to other policies from the literature. We also study shelf life variability and its influence on the system performance. Numerical examples show that in the case of a random shelf life, the developed control policy presents a lower total cost in comparison with other policies from the literature.

Optimal Water Allocation Under Climate Change, Based on Stochastic Dynamic Programming Model Approach in Ribb Reservoir, Amhara, Ethiopia

Optimal Water Allocation Under Climate Change, Based on Stochastic Dynamic Programming Model Approach in Ribb Reservoir, Amhara, Ethiopia

Supported Diagnosis of Attention Deficit and Hyperactivity Disorder from EEG Based on Interpretable Kernels for Hidden Markov Models.

As a neurodevelopmental pathology, Attention Deficit Hyperactivity Disorder (ADHD) mainly arises during childhood. Persistent patterns of generalized inattention, impulsivity, or hyperactivity characterize ADHD that may persist into adulthood. The conventional diagnosis relies on clinical observational processes yielding high rates of overdiagnosis due to varying interpretations among specialists or missing information. Although several studies have designed objective behavioral features to overcome such an issue, they lack significance. Despite electroencephalography (EEG) analyses extracting alternative biomarkers using signal processing techniques, the nonlinearity and nonstationarity of EEG signals restrain performance and generalization of hand-crafted features. This work proposes a methodology to support ADHD diagnosis by characterizing EEG signals from hidden Markov models (HMM), classifying subjects based on similarity measures for probability functions, and spatially interpreting the results using graphic embeddings of stochastic dynamic models. The methodology learns a single HMM for EEG signal from each patient, so favoring the inter-subject variability. Then, the Probability Product Kernel, specifically developed for assessing the similarity between HMMs, fed a support vector machine that classifies subjects according to their stochastic dynamics. Lastly, the kernel variant of Principal Component Analysis provided a means to visualize the EEG transitions in a two-dimensional space, evidencing dynamic differences between ADHD and Healthy Control children. From the electrophysiological perspective, we recorded EEG under the Stop Signal Task modified with reward levels, which considers cognitive features of interest as insufficient motivational circuits recruitment. The methodology compares the supported diagnosis in two EEG channel setups (whole channel set and channels of interest in frontocentral area) and four frequency bands (Theta, Alpha, Beta rhythms, and a wideband). Results evidence an accuracy rate of 97.0% in the Beta band and in the channels where previous works found error-related negativity events. Such accuracy rate strongly supports the dual pathway hypothesis and motivational deficit concerning the pathophysiology of ADHD. It also demonstrates the utility of joining inhibitory and motivational paradigms with dynamic EEG analysis into a noninvasive and affordable diagnostic tool for ADHD patients.

Sparse and smooth: Improved guarantees for spectral clustering in the dynamic stochastic block model

In this paper, we analyze classical variants of the Spectral Clustering (SC) algorithm in the Dynamic Stochastic Block Model (DSBM). Existing results show that, in the relatively sparse case where the expected degree grows logarithmically with the number of nodes, guarantees in the static case can be extended to the dynamic case and yield improved error bounds when the DSBM is sufficiently smooth in time, that is, the communities do not change too much between two time steps. We improve over these results by drawing a new link between the sparsity and the smoothness of the DSBM: the smoother the DSBM is, the more sparse it can be, while still guaranteeing consistent recovery. In particular, a mild condition on the smoothness allows to treat the sparse case with bounded degree. These guarantees are valid for the SC applied to the adjacency matrix or the normalized Laplacian. As a by-product of our analysis, we obtain to our knowledge the best spectral concentration bound available for the normalized Laplacian of matrices with independent Bernoulli entries.

A stochastic viral model with cell-to-cell transmission

The purpose of this paper is to investigate the dynamics of a stochastic virus dynamical model with both cell-to-virus infection and cell-to-cell transmission and cure rate. We show the existence of a unique global positive solution of our system, we give a sufficient conditions for persistence of the infected cells and virus, we prove that the virus-free equilibrium is almost sure exponentially stable. Numerical simulations are achieved to illustrate theoretical results.

Robot Scheduling for Mobile‐Rack Warehouses: Human–Robot Coordinated Order Picking Systems

Intelligent part‐to‐picker systems are spreading across a broad range of industries as preferred solutions for agile order fulfillment, wherein mobile racks are carried by robots and moved to stations where human pickers can pick items from them. Such systems raise the challenge of designing good work schedules for human pickers; they also give rise to a new class of operational scheduling problems in human–robot coordinated order picking systems. This work studies the problem of finding a suitable robot schedule that takes into account the schedule‐induced fluctuation of the working states of human pickers. A proposed model enables mobile racks with various workloads to be assigned to pickers, and schedule the racks that are assigned to every picker to minimize the expected total picking time. The problem is formulated as a stochastic dynamic program model. An approximate dynamic programming (ADP)‐based branch‐and‐price solution approach is used to solve this problem. The developed model is calibrated using data that were collected from a dominant e‐commerce company in China. Pickers' working state transitions are modeled based on data obtained from this warehouse. Counter‐factual studies demonstrate that the proposed approach can solve a moderately sized problem with 50 racks in under 2 minutes. More importantly, the approach yields high‐quality solutions with picking times that are 10% shorter than the solutions that did not consider schedule‐induced fluctuations of pickers' working states.

A study on the estimation of COVID-19 case with the adaptive kalman filter (AKF) in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan

In this paper, COVID-19 cumulative cases are estimated with AKF based on total COVID-19 cases between January-September 9, 2020 in USA, Germany, United Kingdom, Italy, France, Russia, Brazil, India, Turkey, Spain, Peru, Colombia, South Africa, Argentina, Iran, Pakistan. The cumulative covid-19 cases time-series was modeled with a stochastic dynamic linear model (DLM). The estimation performance of the models is measured by the calculation of mean square error (MSE) and coefficient of determination ( ). Calculated MSE and values showed that the model and AKF could be used to estimate the number of cases in these countries. In this study, firstly, the cumulative number of cases was estimated. Secondly, using these estimates number of daily cases was calculated. Thirdly, the reproduction number was obtained by using these number of daily cases. The model and estimation method used is suitable. The AKF algorithm uses only the number of cases in the last day. We propose that the model and estimation method under consideration is a convenient tool for calculating the reproduction number depending on time.

A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-Dependent Maximum Discharge Constraints

A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-Dependent Maximum Discharge Constraints

Stochastic Modeling and Convergence Analysis of Internet Routers

The existent route convergence models are mainly deterministic ones, and various phenomena, such as packet losses, link noises, and sudden changes in interconnecting topology will occur in the route convergence process. Aimed at these random problems, a new stochastic dynamic system model was proposed by means of the Bernoulli white sequence distribution, the Wiener process and the Markov process. Based on the stochastic differential equation theory and the stochastic analysis methods, the sufficient conditions for the route convergence were given. The results prove that, the convergence of the routing state in a random environment is closely related to the Laplacian matrix of the router connection topology, the smooth distribution of the Markov switching, the successful transmission rate of the data packets, and the noise intensity in the network. Finally, a numerical example illustrates the effectiveness of the results.

Pollution and Labor Market Search Externalities Over the Business Cycle

We study the relationship between unemployment, environmental policy, and business cycles. We develop a dynamic stochastic general equilibrium real business cycle model that includes both a pollution externality and congestion externalities from labor market search frictions, which generate unemployment. We consider two policies to address the market failures: an emissions tax and a tax or subsidy on job creation. With both policies present, the efficient outcome can be achieved. When one policy is constrained or absent, we solve for the second best. The absence of a vacancy policy to address the congestion externalities substantially affects the value of the emissions tax, both in steady state and over the business cycle. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles.

Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory

This paper introduces an innovative social process model addressing population-wide measures of voter preferences that was tested on data from the 2016 US presidential election. Population-wide, “macroscopic” parameters are needed when privacy, ethics or regulatory constraints block “big data” techniques (e.g., in political contexts to counter “micro-targeting”). Confidence will be eroded if existing trend models and other macroscopic approaches frequently fail to predict outcomes, however campaign data reveal mathematical features that suggest a different possible approach. Given that the populations modelled exhibit self-organisation and memory when transmitting viewpoints, our model is based on mathematical representations of such processes. Its validation indicates the applicability and potential generalisability of this theoretical approach. In order to design a stochastic dynamics model of changing voter preferences, we evaluated probability models for transitions between possible system states (magnitudes of voter preferences), formulated the boundary task for probability density functions and derived a second-order non-linear differential equation incorporating self-organisation and memory. We find consistent dependencies between influences on the system and its reaction, and it is congruent with empirical data. The ability to use researchable global parameters indicates the potential for modelling electoral processes and wider applicability for complex social processes, avoiding dependence on “internal” variables.

Numerical scheme and analytical solutions to the stochastic nonlinear advection diffusion dynamical model

Abstract In this study, we give the numerical scheme to the stochastic nonlinear advection diffusion equation. This models is considered with white noise (or random process) having same intensity by changing frequencies. Furthermore, the stability and consistency of proposed scheme are also discussed. Moreover, it is concerned about the analytical solutions, the Riccati equation mapping method is adopted. The different families of single (shock and singular) and mixed (complex solitary-shock, shock-singular, and double-singular) form solutions are obtained with the different choices of free parameters. The graphical behavior of solutions is also depicted in 3D and corresponding contours.

The Phase‐Locking of Tropical North Atlantic and the Contribution of ENSO

AbstractThe Tropical North Atlantic (TNA) is characterized by significant interannual variability in sea surface temperature (SST), which is phase‐locked to the boreal spring. In this study, the phase‐locking of TNA is investigated by adopting a linear stochastic‐dynamical model (SDM) using seasonally modulated TNA feedbacks together with the seasonal modulation of ENSO forcing. In the observations, the role of local TNA feedbacks and ENSO forcing in TNA phase‐locking are equivalently important with both preferring the peak of TNA variability to appear in the boreal spring. Besides, the seasonal modulation of TNA feedbacks and ENSO forcing strength are both mainly controlled by thermodynamic processes. In most climate models, the contribution of ENSO on TNA phase‐locking is weaker than that in observations. The strength of ENSO‐related TNA phase‐locking is highly correlated with the relationship between ENSO and TNA, which is mainly determined by the amplitude of ENSO and its teleconnection patterns.

Brain drain or brain gain? International labor mobility and human capital formation

This paper studies the impact of international labor migration on human capital investment in both hosting and sending countries using an integrated theoretical framework. We develop a two-country dynamic stochastic general equilibrium human capital investment model with international labor mobility, in which both decisions to migrate and to invest in skill acquisition are endogenous. We show that the human capital formation process in the countries of origin is very sensitive to migration policies implemented by hosting countries. Our findings show that human capital accumulation in the sending country is encouraged by the possibility of emigration to higher labor productivity countries, supporting the recent view of the ‘brain gain’ hypothesis. Productivity shocks hitting the hosting country reduce the human capital investment by natives but increase the human capital investment in the sending country when migration is allowed. Finally, we find that migration increases world human capital, increasing the stock of human capital in both hosting and sending countries.

Order scoring, bandit learning and order cancellations

This paper develops an order scoring model that quantifies the performance of a limit order before execution. Our dynamic stochastic model takes into account of the bid-ask queue imbalance, the queue position of an order and price dynamics. We calibrate and validate the model using the historical order book data and backtesting simulations, and show that our model can perform well empirically. We also combine our model with multi-armed bandit learning to guide order cancellation decisions. We illustrate the empirical performances of various bandit algorithms and show that the Upper Confidence Bound algorithm generally performs the best.