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Journal articles on the topic 'Dynamic stochastic models'

Author: Grafiati

Published: 1 March 2025

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1

Assaf, A. George, Mike G. Tsionas, and Florian Kock. "Dynamic quantile stochastic frontier models." International Journal of Hospitality Management 89 (August 2020): 102588. http://dx.doi.org/10.1016/j.ijhm.2020.102588.

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2

Dror, Moshe, and Warren Powell. "Stochastic and Dynamic Models in Transportation." Operations Research 41, no. 1 (February 1993): 11–14. http://dx.doi.org/10.1287/opre.41.1.11.

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3

Reichman, David R. "On Stochastic Models of Dynamic Disorder†." Journal of Physical Chemistry B 110, no. 38 (September 2006): 19061–65. http://dx.doi.org/10.1021/jp061992j.

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4

Yano, Makoto. "Comparative statics in dynamic stochastic models." Journal of Mathematical Economics 18, no. 2 (January 1989): 169–85. http://dx.doi.org/10.1016/0304-4068(89)90020-7.

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5

Zilcha, I. "Efficiency in Stochastic Dynamic Economic Models." IFAC Proceedings Volumes 22, no. 5 (June 1989): 357–61. http://dx.doi.org/10.1016/s1474-6670(17)53474-6.

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6

Popkov, Yu S. "Macrosystems Models of Dynamic Stochastic Networks." Automation and Remote Control 64, no. 12 (December 2003): 1956–74. http://dx.doi.org/10.1023/b:aurc.0000008434.58605.1b.

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7

Creal, Drew D., and Ruey S. Tsay. "High dimensional dynamic stochastic copula models." Journal of Econometrics 189, no. 2 (December 2015): 335–45. http://dx.doi.org/10.1016/j.jeconom.2015.03.027.

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8

Fan, Ruzong, Bin Zhu, and Yuedong Wang. "Stochastic dynamic models and Chebyshev splines." Canadian Journal of Statistics 42, no. 4 (November 3, 2014): 610–34. http://dx.doi.org/10.1002/cjs.11233.

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9

Tsionas, Efthymios G. "Inference in dynamic stochastic frontier models." Journal of Applied Econometrics 21, no. 5 (2006): 669–76. http://dx.doi.org/10.1002/jae.862.

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10

Popkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov, and Dimitri Solomatine. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models." Mathematics 8, no. 7 (July 8, 2020): 1119. http://dx.doi.org/10.3390/math8071119.

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We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics.

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11

Dahani, Khawla, and Rajae Aboulaich. "Dynamic Stochastic General Equilibrium model for the Islamic economy." Investment Management and Financial Innovations 15, no. 3 (October 2, 2018): 370–82. http://dx.doi.org/10.21511/imfi.15(3).2018.30.

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This article is concerned with the debate around the economic knowledge evolution and the role of ethics in economy. It reports on the 2008 crisis, the research literature reveals two main problems: the efficiency of the economic modeling and the failure of the ethical system.The authors explore the use of the new Dynamic Stochastic General Equilibrium “DSGE” model in the case of Islamic economy, it can enable to develop a new approach, taking into account the criticism of the models used before the crisis, and giving more importance to the ethical principles.The question is to know if the principles of Islamic economy feed into a sustainable economic system.The characteristic of this model lies in the consideration of Islamic principles, namely the abolition of interest rates and their replacement by the rate of return of the capital. In this perspective, it is supposed that the intervention of the monetary authorities is done by an unconventional approach. The model also distinguishes itself by the integration of Zakat. The model is applied in the case of Morocco.The results of simulations show that the introduction of these Islamic principles has no negative effects on the macroeconomic and financial conditions of Morocco and that the stability of the economic system is maintained.

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12

Martins, Igor, and Hedibert Freitas Lopes. "Stochastic Volatility Models with Skewness Selection." Entropy 26, no. 2 (February 6, 2024): 142. http://dx.doi.org/10.3390/e26020142.

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This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks’ mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

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13

Morales-Jiménez, Camilo. "Dynamic and Stochastic Search Equilibrium." Finance and Economics Discussion Series 2021, no. 055r1 (March 31, 2022): 1–46. http://dx.doi.org/10.17016/feds.2022.018.

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I study the business cycle properties of wage posting models with random search, for which the distributions of employment and wages play a nontrivial role for the equilibrium path. In fact, the main result of this paper is that the distribution of firms is one of the most important elements to understand business cycle fluctuations in the labor market. The distribution of firms (1) determines which shocks are relevant for the labor market, (2) implies that wage rigidity does not significantly amplify shocks, and (3) puts discipline on the relative value of the flow opportunity cost of employment. To assess these type of models quantitatively, I propose a new algorithm that finds the steady state and computes transitional dynamics rapidly. Hence, integrating wage posting models with random search to larger models becomes possible (and easy) with this new algorithm.

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14

Dolgui, Alexandre, and Jean-Marie Proth. "Stochastic Dynamic Pricing Models of Monopoly Systems." IFAC Proceedings Volumes 42, no. 4 (2009): 1469–80. http://dx.doi.org/10.3182/20090603-3-ru-2001.0585.

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15

Asai, Manabu, and Michael McAleer. "Dynamic Asymmetric Leverage in Stochastic Volatility Models." Econometric Reviews 24, no. 3 (July 2005): 317–32. http://dx.doi.org/10.1080/07474930500243035.

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16

Glickman, Mark E. "Dynamic paired comparison models with stochastic variances." Journal of Applied Statistics 28, no. 6 (August 2001): 673–89. http://dx.doi.org/10.1080/02664760120059219.

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17

Santos, Manuel S., and Adrian Peralta-Alva. "Accuracy of Simulations for Stochastic Dynamic Models." Econometrica 73, no. 6 (November 2005): 1939–76. http://dx.doi.org/10.1111/j.1468-0262.2005.00642.x.

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18

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan F. Rubio-Ramírez. "Estimating dynamic equilibrium models with stochastic volatility." Journal of Econometrics 185, no. 1 (March 2015): 216–29. http://dx.doi.org/10.1016/j.jeconom.2014.08.010.

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19

Nyarko, Yaw, and Lars J. Olson. "Stochastic dynamic models with stock-dependent rewards." Journal of Economic Theory 55, no. 1 (October 1991): 161–68. http://dx.doi.org/10.1016/0022-0531(91)90063-a.

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20

Doraszelski, Ulrich, and Juan F. Escobar. "Protocol invariance and the timing of decisions in dynamic games." Theoretical Economics 14, no. 2 (2019): 597–646. http://dx.doi.org/10.3982/te3230.

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We characterize a class of dynamic stochastic games that we call separable dynamic games with noisy transitions and establish that these widely used models are protocol invariant provided that periods are sufficiently short. Protocol invariance means that the set of Markov perfect equilibria is nearly the same irrespective of the order in which players are assumed to move within a period. Protocol invariance can facilitate applied work, and renders the implications and predictions of a model more robust. Our class of dynamic stochastic games includes investment games, research and development races, models of industry dynamics, dynamic public contribution games, asynchronously repeated games, and many other models from the extant literature.

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21

Santonja, Francisco-José, and Leonid Shaikhet. "Analysing Social Epidemics by Delayed Stochastic Models." Discrete Dynamics in Nature and Society 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/530472.

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We investigate the dynamics of a delayed stochastic mathematical model to understand the evolution of the alcohol consumption in Spain. Sufficient condition for stability in probability of the equilibrium point of the dynamic model with aftereffect and stochastic perturbations is obtained via Kolmanovskii and Shaikhet general method of Lyapunov functionals construction. We conclude that alcohol consumption in Spain will be constant (with stability) in time with around 36.47% of nonconsumers, 62.94% of nonrisk consumers, and 0.59% of risk consumers. This approach allows us to emphasize the possibilities of the dynamical models in order to study human behaviour.

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22

Urbina, Angel, and Thomas Paez. "Probabilistic Numerical Analysis of Large, Complex, Structural Dynamic System Models." Journal of the IEST 46, no. 1 (September 14, 2003): 119–27. http://dx.doi.org/10.17764/jiet.46.1.p3k33743858u56hx.

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In recent years, great progress has been made in the construction and solution of large finite element models of complex structural dynamic systems. For example, structural models with millions of degrees of freedom are being built and used to approximate responses of structural systems. Further, great progress is being made in stochastic system analysis. Techniques for the construction of stochastic system models have been developed and solution techniques proposed. However, the two areas have not been combined, on a large scale, because stochastic finite element approaches appear very intrusive in their pure form. That is, substantial modifications of deterministic finite element codes are required to accommodate stochastic analysis. In view of this, a technique that uses the techniques of stochastic finite elements in a non-intrusive manner is required. This research provides one such approach. Specifically, the problem is divided into three parts: (1) model structural dynamic excitations using traditional approaches, and model physical system randomness using techniques of stochastic finite elements, namely, the Karhunen-Loeve expansion and polynomial chaos; (2) generate stochastic structural realizations and realizations of the random excitation using a Monte Carlo approach, and analyze structural responses with parallel computation in a suitable, large-scale finite element code; and (3) analyze structural dynamic responses using the techniques of stochastic finite elements, namely, the Karhunen-Loeve expansion and polynomial chaos. This paper supplies the details of the analytical approach. A numerical example is presented.

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23

Xing, Pengfei, Feng Zhao, Xiaoliang He, and Guobin Li. "Investigation on Dynamic Behaviors of Ship Propulsion Shafting with Misalignment Based on Stochastic Uncertainty Models." Journal of Marine Science and Engineering 12, no. 11 (October 28, 2024): 1927. http://dx.doi.org/10.3390/jmse12111927.

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To investigate the effect of stochastic uncertainty on the dynamic behaviors of ship propulsion shafting with misalignment, a stochastic uncertainty model of ship shafting is established based on nonparametric theory and stochastic excitation. Numerical simulation and experimental verification of the dynamic behaviors are carried out using a ship shafting test bench. The results indicate that stochastic uncertainty has a significant effect on the dynamic behaviors of shafting with misalignment. With an increase in stochastic uncertainty, the fractional frequency appears in the spectrum, and the axis trajectory becomes more complex and gradually deviates from the center orbit. Therefore, to ensure the safe navigation of ships, it is necessary to consider the stochastic uncertainty in dynamic research on the misalignment of shafting.

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24

Wang, Haibo, Yongfeng Cheng, Zhicheng Lu, Ronghua Huan, Qiangfeng Lü, and Zhenlin Liu. "Stochastic Response of Composite Post Insulators under Seismic Excitation." Buildings 14, no. 6 (May 25, 2024): 1539. http://dx.doi.org/10.3390/buildings14061539.

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Composite post insulators are crucial facilities in substations and are prone to significant damage during seismic disasters. However, existing research lacks seismic motion models suitable for power facilities and rarely involves stochastic models. Furthermore, considering the non-stationary characteristics of seismic motion, predicting the response of nonlinear systems under non-stationary excitation becomes exceedingly challenging. In view of this, the stochastic response of composite post insulators under the non-stationary stochastic seismic excitation appropriate for power facilities has been studied. First, a stochastic ground motion model, conforming to the Code for Seismic Design of Electrical Insulators in China, is established, incorporating amplitude and frequency non-stationarity. Next, the nonlinear dynamic system, accounting for multi-section composite post insulators and the nonlinearity of flange connections, is established under stochastic ground motion conditions. Based on this stochastic nonlinear dynamic model, the dynamic behavior of the system was analyzed using the stochastic dynamics method (the wavelet-Galerkin method), and the influence of nonlinear stiffness on the system response was discussed. The stochastic seismic response analysis method proposed in this paper can serve as a valuable reference for the seismic design of pillar-type electrical equipment.

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25

Harrison, L. M., O. David, and K. J. Friston. "Stochastic models of neuronal dynamics." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1457 (May 29, 2005): 1075–91. http://dx.doi.org/10.1098/rstb.2005.1648.

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Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have already been established for deterministic systems. The potential importance of modelling density dynamics (as opposed to more conventional neural mass models) is that they include interactions among the moments of neuronal states (e.g. the mean depolarization may depend on the variance of synaptic currents through nonlinear mechanisms). Here, we formulate a population model, based on biologically informed model-neurons with spike-rate adaptation and synaptic dynamics. Neuronal sub-populations are coupled to form an observation model, with the aim of estimating and making inferences about coupling among sub-populations using real data. We approximate the time-dependent solution of the system using a bi-orthogonal set and first-order perturbation expansion. For didactic purposes, the model is developed first in the context of deterministic input, and then extended to include stochastic effects. The approach is demonstrated using synthetic data, where model parameters are identified using a Bayesian estimation scheme we have described previously.

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26

Bod’ová, Katarína, Enikő Szép, and Nicholas H. Barton. "Dynamic maximum entropy provides accurate approximation of structured population dynamics." PLOS Computational Biology 17, no. 12 (December 1, 2021): e1009661. http://dx.doi.org/10.1371/journal.pcbi.1009661.

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Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment.

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27

Avramenko, Olga, and Volodymyr Naradovyi. "Weakly nonlinear models of stochastic wave propagation in two-layer hydrodynamic systems." Mohyla Mathematical Journal 6 (April 18, 2024): 39–44. http://dx.doi.org/10.18523/2617-70806202339-44.

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The paper discusses three-dimensional models of the propagation of stochastic internal waves in hydrodynamic systems: ’half-space - half-space’, ’half-space - layer with rigid lid’, and ’layer with solid bottom - layer with rigid lid’. In constructing the models, the layers are considered to be ideal fluids separated by a contact surface. The main objective of the modeling is to obtain a dynamic equation for the stochastic amplitude of surface waves. A comparative analysis of the obtained results has been conducted. In order to control the contribution of nonlinear terms, a dimensionless non-numerical parameter has been introduced. The models are distinguished by boundary conditions that determine the general form of solutions. As a result, a dynamic equation for the stochastic amplitude of internal waves has been derived. After ensemble averaging of the amplitudes, the dynamic equation is formulated in integral form using Fourier-Stieltjes integrals. The dynamic equation reveals two-wave and three-wave interactions, as well as the contribution of dispersion to wave dynamics. An investigation of the boundary case of the transition of internal waves in the ’half-space - half-space’ system to surface waves in the absence of an upper liquid layer confirms the validity of the results.

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28

Arbeev, Konstantin, Olivia Bagley, Arseniy Yashkin, Hongzhe Duan, Igor Akushevich, Svetlana Ukraintseva, and Anatoliy Yashin. "Applications of Stochastic Process Models to Constructing Predictive Models of Alzheimer’s Disease." Innovation in Aging 4, Supplement_1 (December 1, 2020): 263. http://dx.doi.org/10.1093/geroni/igaa057.844.

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Abstract Large-scale population-based data collecting repeated measures of biomarkers, follow-up data on events (incidence of diseases and mortality), and extensive genetic data provide excellent opportunities for applying statistical models for joint analyses of longitudinal dynamics of biomarkers and time-to-event outcomes that allow investigating dynamics of biomarkers and other relevant factors (including genetic) in relation to risks of diseases and death and how this may propagate to the future. Here we applied one such model, the stochastic process model (SPM), to data on longitudinal trajectories of different variables (comorbidity index, body mass index, cognitive scores), other relevant covariates (including genetic factors such as APOE polymorphisms and polygenic scores, PGS), and data on onset of Alzheimer’s disease (AD) in the Health and Retirement Study. We observed that different aging-related characteristics estimated from trajectories of respective variables in SPM are strongly associated with risks of onset of AD and found that these associations differ by sex, APOE status (carriers vs. non-carriers of APOE e4) and by PGS groups. The approach allows modeling and estimating time trends (e.g., by birth cohorts) in relevant dynamic characteristics in relation to the disease onset. These results provide building blocks for constructing the models for forecasting future trends and burden of AD that take into account dynamic relationships between individual trajectories of relevant repeatedly measured characteristics and the risk of the disease. Such models also provide the analytic framework for understanding AD in the context of aging and for finding genetic underpinnings of such links between AD and aging.

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29

Zarrop, M. B. "Book Review: Dynamic Programming: Deterministic and Stochastic Models." International Journal of Electrical Engineering & Education 25, no. 4 (October 1988): 376–77. http://dx.doi.org/10.1177/002072098802500429.

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30

Cardwell, Hal, and Hugh Ellis. "Stochastic dynamic programming models for water quality management." Water Resources Research 29, no. 4 (April 1993): 803–13. http://dx.doi.org/10.1029/93wr00182.

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31

ĆMIEL, ADAM, and HENRYK GURGUL. "Dynamic input-output models with stochastic time lags." International Journal of Systems Science 27, no. 9 (September 1996): 857–61. http://dx.doi.org/10.1080/00207729608929286.

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32

Bartolucci, Francesco, Maria Francesca Marino, and Silvia Pandolfi. "Dealing with reciprocity in dynamic stochastic block models." Computational Statistics & Data Analysis 123 (July 2018): 86–100. http://dx.doi.org/10.1016/j.csda.2018.01.010.

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33

Kamenskaya, V., and L. Tomanov. "Stochastic principles in dynamic models of the brain." International Journal of Psychophysiology 131 (October 2018): S23—S24. http://dx.doi.org/10.1016/j.ijpsycho.2018.07.075.

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34

Triantafyllopoulos, K. "Multivariate stochastic volatility with Bayesian dynamic linear models." Journal of Statistical Planning and Inference 138, no. 4 (April 2008): 1021–37. http://dx.doi.org/10.1016/j.jspi.2007.03.057.

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35

Schenk, C. A., H. J. Pradlwarter, and G. I. Schuëller. "On the dynamic stochastic response of FE models." Probabilistic Engineering Mechanics 19, no. 1-2 (January 2004): 161–70. http://dx.doi.org/10.1016/j.probengmech.2003.11.013.

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36

Schoder, Christian. "Are Dynamic Stochastic Disequilibrium models Keynesian or neoclassical?" Structural Change and Economic Dynamics 40 (March 2017): 46–63. http://dx.doi.org/10.1016/j.strueco.2016.11.004.

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37

Picci, G. "A Theory of Dynamic Aggregation by Stochastic Models." IFAC Proceedings Volumes 23, no. 8 (August 1990): 305–8. http://dx.doi.org/10.1016/s1474-6670(17)52113-8.

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38

Bowsher, Clive G. "Stochastic kinetic models: Dynamic independence, modularity and graphs." Annals of Statistics 38, no. 4 (August 2010): 2242–81. http://dx.doi.org/10.1214/09-aos779.

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39

Ruge-Murcia, Francisco J. "Methods to estimate dynamic stochastic general equilibrium models." Journal of Economic Dynamics and Control 31, no. 8 (August 2007): 2599–636. http://dx.doi.org/10.1016/j.jedc.2006.09.005.

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40

Emvudu, Yves, Danhrée Bongor, and Rodoumta Koïna. "Mathematical analysis of HIV/AIDS stochastic dynamic models." Applied Mathematical Modelling 40, no. 21-22 (November 2016): 9131–51. http://dx.doi.org/10.1016/j.apm.2016.05.007.

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41

Hutchinson, John M. C., and John M. McNamara. "Ways to test stochastic dynamic programming models empirically." Animal Behaviour 59, no. 4 (April 2000): 665–76. http://dx.doi.org/10.1006/anbe.1999.1362.

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42

Hafner, Christian M., and Hans Manner. "Dynamic stochastic copula models: estimation, inference and applications." Journal of Applied Econometrics 27, no. 2 (June 30, 2010): 269–95. http://dx.doi.org/10.1002/jae.1197.

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43

Duso, Lorenzo, and Christoph Zechner. "Stochastic reaction networks in dynamic compartment populations." Proceedings of the National Academy of Sciences 117, no. 37 (August 31, 2020): 22674–83. http://dx.doi.org/10.1073/pnas.2003734117.

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Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.

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44

Blueschke-Nikolaeva, V., D. Blueschke, and R. Neck. "OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems." Computational Economics 56, no. 1 (December 9, 2019): 145–62. http://dx.doi.org/10.1007/s10614-019-09949-0.

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AbstractIn this paper, we describe the new OPTCON3 algorithm, which serves to determine approximately optimal policies for stochastic control problems with a quadratic objective function and nonlinear dynamic models. It includes active learning and the dual effect of optimizing policies, whereby optimal policies are used to learn about the stochastics of the dynamic system in addition to their immediate effect on the performance of the system. The OPTCON3 algorithm approximates the nonlinear model with a time-varying linear model and applies a procedure similar to that of Kendrick to the series of linearized models to calculate approximately optimal policies. The results for two simple economic models serve to test the OPTCON3 algorithm and compare it to previous solutions of the stochastic control problem. Initial evaluations show that the OPTCON3 approach may be promising to enhance our understanding of the adaptive economic policy problem under uncertainty.

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45

Stachurski, John. "BOUNDING TAIL PROBABILITIES IN DYNAMIC ECONOMIC MODELS." Macroeconomic Dynamics 16, S1 (December 30, 2011): 117–26. http://dx.doi.org/10.1017/s136510051100054x.

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This paper provides conditions for bounding tail probabilities in stochastic economic models in terms of their transition laws and shock distributions. Particular attention is given to conditions under which the tails of stationary equilibria have exponential decay. By way of illustration, the technique is applied to a threshold autoregression model of exchange rates.

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46

EL QALLI, YASSINE. "RECURSIVE BAYESIAN ESTIMATION IN FORWARD PRICE MODELS IMPLIED BY FAIR PRICING." International Journal of Theoretical and Applied Finance 13, no. 02 (March 2010): 301–33. http://dx.doi.org/10.1142/s0219024910005784.

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In this paper we describe a recursive Bayesian algorithm for the estimation of forward price models. The forward price is modeled within the benchmark framework for a forward price volatility function which includes a stochastic variable; a forward price with a liquidly traded maturity. A relationship between the bond price, the spot price and certain forward prices is stated. We set up the stochastic real world dynamics for these discretely compounded market observed forward prices. We propose a dynamic Bayesian estimation algorithm for a Monte Carlo time-discretized version of the resulting forward prices dynamics. The parameter to be estimated is a vector consisting of the forward price volatility parameters and the benchmarked bond price volatility parameters.

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47

Žiniauskaitė, Eugenija, and Vitalijus Denisovas. "Weather forecast for simulation models of agroecosystem production." Lietuvos matematikos rinkinys, no. II (December 14, 1998): 334–41. https://doi.org/10.15388/lmd.1998.37928.

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The forecast of the agroecosystem fate including the rate of crop development and expected yield are considered as the basic tasks of the dynamic agroecological modelling system that has been developed at the University of Klaipėda.This paper describes the development and adaptation in Lithuania of the stochastic weather simulator. Once the parameters of the simulator are identified it allows the user to simulate the weather conditions of the current vegetation period, and therefore, to use the whole modelling system for calculations of various prognostic scenario.The dynamics of main daily weather elements such as min and max daily temperatures, relative air humidity, cloudiness, precipitation and wind speed are described by discrete time stochastic processes. The first four elements are considered as the four-dimensional discrete time process while two others are modelled separately. Parameter identification as well as weather generation procedures are described and the verification of the stochastic weather simulator is carried out using the data from Lithuanian state hydrometeorological network.

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Maass, Wolfgang, and Anthony M. Zador. "Dynamic Stochastic Synapses as Computational Units." Neural Computation 11, no. 4 (May 1, 1999): 903–17. http://dx.doi.org/10.1162/089976699300016494.

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In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.

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49

Ye, Hongbo. "On Stochastic-User-Equilibrium-Based Day-to-Day Dynamics." Transportation Science 56, no. 1 (January 2022): 103–17. http://dx.doi.org/10.1287/trsc.2021.1080.

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Researchers have proposed many different concepts and models to study day-to-day dynamics. Some models explicitly model travelers’ perceiving and learning on travel costs, and some other models do not explicitly consider the travel cost perception but instead formulate the dynamics of flows as the functions of flows and measured travel costs (which are determined by flows). This paper investigates the interconnection between these two types of day-to-day models, in particular, those models whose fixed points are a stochastic user equilibrium. Specifically, a widely used day-to-day model that combines exponential-smoothing learning and logit stochastic network loading (called the logit-ESL model in this paper) is proved to be equivalent to a model based purely on flows, which is the logit-based extension of the first-in-first-out dynamic of Jin [Jin W (2007) A dynamical system model of the traffic assignment problem. Transportation Res. Part B Methodological 41(1):32–48]. Via this equivalent form, the logit-ESL model is proved to be globally stable under nonseparable and monotone travel cost functions. Moreover, the model of Cantarella and Cascetta is shown to be equivalent to a second-order dynamic incorporating purely flows and is proved to be globally stable under separable link cost functions [Cantarella GE, Cascetta E (1995) Dynamic processes and equilibrium in transportation networks: Towards a unifying theory. Transportation Sci. 29(4):305–329]. Further, other discrete choice models, such as C-logit, path-size logit, and weibit, are introduced into the logit-ESL model, leading to several new day-to-day models, which are also proved to be globally stable under different conditions.

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Guatteri, M. "Strong Ground-Motion Prediction from Stochastic-Dynamic Source Models." Bulletin of the Seismological Society of America 93, no. 1 (February 1, 2003): 301–13. http://dx.doi.org/10.1785/0120020006.

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