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Gotowe bibliografie tematyczne / Dynamic modelling

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Gotowa bibliografia na temat „Dynamic modelling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Dynamic modelling"

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Wehrli, P., and J. Tödtli. "Dynamic behaviour modelling." Batiment International, Building Research and Practice 14, no. 1 (January 1986): 51–54. http://dx.doi.org/10.1080/01823328608726717.

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Friston, K. J., L. Harrison, and W. Penny. "Dynamic causal modelling." NeuroImage 19, no. 4 (August 2003): 1273–302. http://dx.doi.org/10.1016/s1053-8119(03)00202-7.

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Zirka, S. E., Y. I. Moroz, P. Marketos, and A. J. Moses. "Dynamic hysteresis modelling." Physica B: Condensed Matter 343, no. 1-4 (January 2004): 90–95. http://dx.doi.org/10.1016/j.physb.2003.08.036.

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Lütkepohl, Helmut. "Nonparametric dynamic modelling." Journal of Econometrics 81, no. 1 (November 1997): 1–5. http://dx.doi.org/10.1016/s0304-4076(97)00029-8.

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Mynett, A. "Modelling dynamic behaviour." Trends in Biochemical Sciences 13, no. 5 (May 1988): 189–90. http://dx.doi.org/10.1016/0968-0004(88)90150-8.

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Saha, S. K., S. V. Shah, and P. V. Nandihal. "Evolution of the DeNOC-based dynamic modelling for multibody systems." Mechanical Sciences 4, no. 1 (January 31, 2013): 1–20. http://dx.doi.org/10.5194/ms-4-1-2013.

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Abstract. Dynamic modelling of a multibody system plays very essential role in its analyses. As a result, several methods for dynamic modelling have evolved over the years that allow one to analyse multibody systems in a very efficient manner. One such method of dynamic modelling is based on the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices. The DeNOC-based methodology for dynamics modelling, since its introduction in 1995, has been applied to a variety of multibody systems such as serial, parallel, general closed-loop, flexible, legged, cam-follower, and space robots. The methodology has also proven useful for modelling of proteins and hyper-degree-of-freedom systems like ropes, chains, etc. This paper captures the evolution of the DeNOC-based dynamic modelling applied to different type of systems, and its benefits over other existing methodologies. It is shown that the DeNOC-based modelling provides deeper understanding of the dynamics of a multibody system. The power of the DeNOC-based modelling has been illustrated using several numerical examples.

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Gerya, Taras V., David Fossati, Curdin Cantieni, and Diane Seward. "Dynamic effects of aseismic ridge subduction: numerical modelling." European Journal of Mineralogy 21, no. 3 (June 29, 2009): 649–61. http://dx.doi.org/10.1127/0935-1221/2009/0021-1931.

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Zeinali, Meysar, and Leila Notash. "FUZZY LOGIC-BASED INVERSE DYNAMIC MODELLING OF ROBOT MANIPULATORS." Transactions of the Canadian Society for Mechanical Engineering 34, no. 1 (March 2010): 137–50. http://dx.doi.org/10.1139/tcsme-2010-0009.

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This paper presents the design and implementation of a systematic fuzzy modelling methodology for the inverse dynamic modelling of robot manipulators. The fuzzy logic modelling methodology is motivated in part by the difficulties encountered in the modelling of complex nonlinear uncertain systems, and by the objective of developing an efficient dynamic model for the real-time model-based control. The methodology is applied to build the fuzzy logic-based inverse dynamic model of a prototyped wire-actuated parallel manipulator with uncertain dynamics. The developed inverse dynamics has been used in a fuzzy model-based adaptive robust controller for the tracking control of the parallel manipulator.

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Verlan, A. A., and Jo Sterten. "Intelligent Object-Oriented Approach to Dynamic Energy Systems’ Modelling." Mathematical and computer modelling. Series: Technical sciences, no. 21 (November 2, 2020): 43–51. http://dx.doi.org/10.32626/2308-5916.2020-21.43-51.

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Šleger, V. "Potential use of program Dynamic Designer in spring modelling." Research in Agricultural Engineering 50, No. 1 (February 8, 2012): 1–5. http://dx.doi.org/10.17221/4918-rae.

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Program Dynamic Designer serves for kinematic and dynamical analysis of rigid body system. Models of springs and relations between force and spring deformation can be chosen and inserted into the system. In the article there is presented <br />a model of flexibly fastened body. Result of dynamical analysis is dependence of selected point deflection on time (Figs. 5, 7, 8, 10). The results can be put to use in the design of spring-loaded parts of agricultural machines.

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Rozprawy doktorskie na temat "Dynamic modelling"

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Filion, Yves R. "Extended dynamic network modelling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ58667.pdf.

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Blanco, Blas. "Railway track dynamic modelling." Licentiate thesis, KTH, Farkost och flyg, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207180.

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The railway vehicles are an increasing mean of transportation due to, its reduced impact on environment and high level of comfort provided. These reasons have contributed to settle a positive perception of railway traffic into the European society. In this upward context, the railway industrial sector tackles some important challenges; maintaining low operational costs and controlling the nuisance by-products of trains operation, the most important being railway noise. Track dynamic plays a main role for both issues, since a significant part of the operational costs are associated with the track maintenance tasks and, the noise generated by the track can be dominant in many operational situations. This explains why prediction tools are highly valued by railway companies. The work presented in this licentiate thesis proposes methodologies for accurate and efficient modelling of railway track dynamics. Two core axes have led the development of this task, on one hand, the rail modelling and, on the other hand, the characterisation of the finite length nature of track supports. Firstly, concerning the rail modelling technique, it has evolved under two major premises. On one hand, regarding the frequency domain, it should describe high frequency behaviour of the rail. In order to accomplish with this first premise, a model based on Timoshenko beam theory is used, which can accurately account for the vertical rail behaviour up to 2500 Hz. On the other hand, with respect to the time domain, the response should be smooth and free of discontinuities. This last condition is fulfilled by implementation of the Timoshenko local deformation. Secondly, a model of support that considers its finite length nature is sought. For this purpose, a Timoshenko element over elastic foundation is formulated. Thus, the common model of support, which is based on a concentrated connection, is substituted by a distributed model of support. In this way, several enhancements are achieved; the temporal contact force response is smoothed and a more realistic shape is obtained, the amplitude of the displacement due to the parametric excitation is reduced and the magnitude associated to the ‘pin-pin’ frequency is not overestimated.<br><p>QC 20170522</p>

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Chen, C. C. "Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18517/.

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Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses.

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Göl, Özdemir. "Dynamic modelling of induction machines /." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09PH/09phg595.pdf.

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Clements, Michael P. "Cointegration and dynamic econometric modelling." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334980.

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Qin, Yong. "Dynamic modelling of combustion processes." Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/55453/.

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Neural aided combustion oscillator models (NACO) have been developed for time domain prediction of low frequency oscillations. The NACO method consists of two stages, one is the design of a core-stimulation (CS) model based on the co.

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Somarathne, Shini. "Dynamic thermal modelling using CFD." Thesis, Brunel University, 2003. http://bura.brunel.ac.uk/handle/2438/5523.

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Buildings expend vast quantities of energy, which has a detrimental impact on the environment. Buildings systems are often oversized to cope with possible extreme environmental conditions. Building simulation provides an opportunity to improve building thermal design, but the available tools are typically used in combination in order to overcome their individual deficiencies. Two such tools, often used in tandem are computational fluid dynamics (CFD) and dynamic thermal modelling (DTM). DTM provides a coarse analysis, by considering external and internal thermal conditions over a building (including its fabric) over time. CFD is usually used to provide steady state analysis. Boundary conditions typically in the form of surface temperatures are manually input from DTM into CFD. CFD can model buildings dynamically, but is not commonly used, since solving for hugely different time constants of solid and air pose significant limitations, due to data generated and time consumed. A technique is developed in this study to tackle these limitations. There are two main strands to the research. DTM techniques had to be incorporated into CFD, starting from first principles of modelling heat transfer through solid materials. These were developed into employing the use of functions such as the 'freeze flow' function (FEF) and the 'boundary freeze' function (BFF) in combination with a time-varying grid schedule to model solids and air simultaneously. The FFF pauses the solution of all governing equations of fluid flow, except temperature. The BFF can be applied to solid boundaries to lock their temperatures whilst all other equations are solved. After extensive research the established DTM-CFD Procedure eventually used the FEF and BFF with transient periods and steady state updates, respectively. The second strand of research involved the application of the DTM-CFD Procedure to a typical office space over a period of 24-hours. Through inter-model comparisons with a fully transient simulation, the DTM-CFD Procedure proved to be capable of providing dynamic thermal simulations 16.4% more efficiently than a typical CFD code and more accurately than a typical DTM code. Additional research is recommended for the further improvement of the DTM-CFD Procedure.

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Hinchliffe, Mark. "Dynamic modelling using genetic programming." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391407.

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Crump, Timothy. "Modelling dynamic cracking of graphite." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/modelling-dynamic-cracking-of-graphite(71e81d6f-e712-458c-aa48-0a256749258a).html.

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Advances in dynamic fracture modelling have become more frequent due to increases in computer speed, meaning that its application to industrial problems has become viable. From this, the author has reviewed current literature in terms of graphite material properties, structural dynamics, fracture mechanics and modelling methodologies to be able to address operational issues related to the ageing of Advanced Gas-cooled Reactor (AGR) cores. In particular, the experimentally observed Prompt Secondary Cracking (PSC) of graphite moderator bricks which has yet to be observed within operational reactors, with the objective of supporting their plant life extension. A method known as eXtended Finite Element Method with Cohesive Zones (XCZM) was developed within Code_Aster open-source FEM software. This enabled the incorporation of velocity toughening, irradiation-induced material degradation effects and multiple 3D dynamic crack initiations, propagations and arrests into a single model, which covers the major known attributes of the PSC mechanism. Whilst developing XCZM, several publications were produced. This started with first demonstrating XCZM's ability to model the PSC mechanism in 2D and consequently that methane holes have a noticeable effect on crack propagation speeds. Following on from this, XCZM was benchmarked in 2D against literature experiments and available model data which consequently highlighted that velocity toughening was an integral feature in producing energetically correct fracture speeds. Leading on from this, XCZM was taken into 3D and demonstrated that it produced experimentally observed bifurcation angle from a literature example. This meant that when a 3D graphite brick was modelled that the crack profile was equivalent to an accepted quasi-static profile. As a consequence of this validation, the XCZM approach was able to model PSC and give insight into features that could not be investigated previously including: finer-scale heterogeneous effects on a dynamic crack profile, comparison between Primary and Secondary crack profiles and also, 3D crack interaction with a methane hole, including insight into possible crack arrest. XCZM was shown to improve upon previous 2D models of experiments that showed the plausibility of PSC; this was achieved by eliminating the need for user intervention and also incorporation of irradiation damage effects through User-defined Material properties (UMAT). Finally, while applying XCZM to a full-scale 3D graphite brick including reactor effects, it was shown that PSC is likely to occur under LEFM assumptions and that the Secondary crack initiates before the Primary crack arrests axially meaning that modal analysis would not be able to fully model PSC.

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Alfaer, Nada Mansour. "Dynamic modelling for image analysis." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/21215/.

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Image segmentation is an important task in many image analysis applications, where it is an essential first stage before further analysis is possible. The levelset method is an implicit approach to image segmentation problems. The main advantages are that it can handle an unknown number of regions and can deal with complicated topological changes in a simple and natural way. The research presented in this thesis is motivated by the need to develop statistical methodologies for modelling image data through level sets. The fundamental idea is to combine the level-set method with statistical modelling based on the Bayesian framework to produce an attractive approach for tackling a wider range of segmentation problems in image analysis. A complete framework for a Bayesian level set model is given to allow a wider interpretation of model components. The proposed model is described based on a Gaussian likelihood and exponential prior distributions on object area and boundary length, and an investigation of uncertainty and a sensitivity analysis are carried out. The model is then generalized using a more robust noise model and more flexible prior distributions. A new Bayesian modelling approach to object identification is introduced. The proposed model is based on the level set method which assumes the implicit representation of the object outlines as a zero level set contour of a higher dimensional function. The Markov chain Monte Carlo (MCMC) algorithm is used to estimate the model parameters, by generating approximate samples from the posterior distribution. The proposed method is applied to simulated and real datasets. A new temporal model is proposed in a Bayesian framework for level-set based image sequence segmentation. MCMC methods are used to explore the model and to obtain information about solution behaviour. The proposed method is applied to simulated image sequences.

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Książki na temat "Dynamic modelling"

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Jeffrey, Wix, and Building Services Research and Information Association. Computer Centre., eds. Dynamic thermal modelling. Lutterworth, UK: Ambient Press, 1987.

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Filion, Yves R. Extended dynamic network modelling. Ottawa: National Library of Canada, 2001.

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Dykes, Alan P., Mark Mulligan, and John Wainwright, eds. Monitoring and Modelling Dynamic Environments. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118649596.

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Kulikov, Gennady G., and Haydn A. Thompson, eds. Dynamic Modelling of Gas Turbines. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3796-2.

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1951-, Sol H. G., Hee, Kees Max van, 1946-, and International Working Conference on Dynamic Modelling of Information Systems (1990 : Noordwijkerhout, Netherlands), eds. Dynamic modelling of information systems. Amsterdam: North-Holland, 1991.

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John, Wainwright, Alan P. Dykes, and Mark Mulligan. Monitoring and modelling dynamic environments. West Sussex, UK: John Wiley & Sons, Ltd, 2016.

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Evertsz, Rick, John Thangarajah, and Thanh Ly. Practical Modelling of Dynamic Decision Making. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95195-9.

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Jokar Arsanjani, Jamal. Dynamic land use/cover change modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23705-8.

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Crespo Márquez, Adolfo. Dynamic Modelling for Supply Chain Management. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-681-6.

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Armstrong, P. K. Dynamic modelling and simulation with Numerator. Loughborough: Department of Mathematical Sciences, Loughborough University of Technology, 1991.

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Części książek na temat "Dynamic modelling"

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Nicola, PierCarlo. "Dynamic Modelling." In Mainstream Mathematical Economics in the 20th Century, 61–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04238-0_7.

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Yu, Kam. "Dynamic Modelling." In Springer Texts in Business and Economics, 177–206. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27289-0_9.

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Bissell, C. C. "Modelling dynamic systems." In Control Engineering, 17–49. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-7224-8_3.

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Bissell, C. C. "Modelling Dynamic Systems." In Control Engineering, 17–49. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-9711-7_3.

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Signoret, Jean-Pierre, and Alain Leroy. "Dynamic Modelling Exercises." In Springer Series in Reliability Engineering, 661–706. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64708-7_34.

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George, Donald A. R. "Dynamic optimisation." In Mathematical Modelling for Economists, 113–34. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-19238-0_7.

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Pennanen, Teemu, and Ari-Pekka Perkkiö. "Dynamic Programming." In Probability Theory and Stochastic Modelling, 61–168. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76432-5_2.

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Olafsson, S. "Dynamic Task Allocation." In Modelling Future Telecommunications Systems, 285–310. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2049-8_16.

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Kõvamees, Erik. "Semiosis and Modelling." In Explorations in Dynamic Semiosis, 99–134. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47001-1_3.

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Garrido, José Luis, and Miguel Gea. "Modelling Dynamic Group Behaviours." In Interactive Systems: Design, Specification, and Verification, 128–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45522-1_8.

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Streszczenia konferencji na temat "Dynamic modelling"

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Frye, J. P., and B. C. Fabien. "Modeling and Simulation of Nonholonomic Lagrangian Dynamic Systems." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-057.

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Bae, T. S., J. K. Kim, S. K. Kim, J. C. Ryu, and and M. S. Mun. "Dynamic Simulation of Lumbar Lordortic Model during Isokinetic Exercise." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-123.

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Martínez-Vargas, A., and Á. G. Andrade. "On Evaluation of Dynamic Spectrum Access in Heterogeneous Networks." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.698-020.

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Cicirelli, Franco, Angelo Furfaro, Libero Nigro, and Francesco Pupo. "Dynamic Sociality Minority Game." In 25th Conference on Modelling and Simulation. ECMS, 2011. http://dx.doi.org/10.7148/2011-0027-0033.

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Nguyen, Thang, Christopher Edwards, Guido Herrmann, Toshiaka Hatano, Stuart C. Burgess, and Mervyn Miles. "Cantilever dynamics modelling for the Transverse Dynamic Force Microscope." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039564.

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Nagori, M. B., W. Gore Ranjana, and Madhuri Joshi. "Dynamic causal modelling for schizophrenia." In 2011 International Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2011. http://dx.doi.org/10.1109/shuser.2011.6008504.

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Druet, Krzysztof. "Approaches to Dynamic Tribosystem Modelling." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63771.

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Predictive models for friction and wear are among the most important issues setting guidelines for tribological research [5]. The analysis of published models of tribological phenomena reveals, that the achievement of satisfactory predictive accuracy of the proposed models is still problematic [10, 12]. Practically speaking the mathematical models mentioned have a limited usability for the purpose of designing new machines. Two groups of models used in tribology can be distinguished: design practice oriented and fundamental [10]. The model belonging to the first group is built so it calculates the expected behaviour of the tribological system on the basis of input data. The core of the model is a ‘black box’ the characteristics of which is based on experience. Data gathered in laboratory and field experiments is used by the designer to predict friction and wear on the design stage of a real life tribosystem. Such a model will nowadays have a form of a computer program allowing to calculate wear rates etc. Whereas the fundamental model quantifies various physical processes influencing friction and wear in the analysed system.

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Kotian, Shashidhara Mecha, and K. N. Shubhanga. "Dynamic phasor modelling and simulation." In 2015 Annual IEEE India Conference (INDICON). IEEE, 2015. http://dx.doi.org/10.1109/indicon.2015.7443446.

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"MODELLING KNOWLEDGE FOR DYNAMIC SERVICE DEPLOYMENT - Autonomic Networks Modelling." In 3rd International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001885601540159.

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Hauge, Espen, John-Morten Godhavn, Øyvind N. Stamnes, and Ole Morten Aamo. "Dynamic Modelling of Gas Rising in a Wellbore." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-045.

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Raporty organizacyjne na temat "Dynamic modelling"

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Tatlicioglu, E., Ian D. Walker, and Darren M. Dawson. Dynamic Modelling for Planar Extensible Continuum Robot Manipulators. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada462495.

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Athow, Jonathan. Incorporating behavioural change and dynamic considerations in tax policy modelling. The IFS, October 2012. http://dx.doi.org/10.1920/ps.ifs.2024.0869.

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Chand, K., W. Henshaw, and T. Vassilevska. Reduced order modelling for dynamic simulations: LDRD feasibility study final report. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1059464.

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Leão, Emanuel, and Pedro Leão. Modelling The Central Bank Repo Rate In a Dynamic General Equilibrium Framework. DINÂMIA'CET-IUL, 2005. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2005.42.

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Ho, Yu-Chi, and Wei-Bo Gong. Discrete Event Dynamic Systems Modelling and Optimization with Applications to C3I Problems. Fort Belvoir, VA: Defense Technical Information Center, August 1995. http://dx.doi.org/10.21236/ada300786.

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Bello, Paul, and Mitch Mailman. Applications of Dynamic Systems Theory to Effects-Based Operations and Adversarial Modelling. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada465801.

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Hall, Stephen. Time-Series Methods: Dynamic Modeling, Non-Stationarity, and Cointegration. Instats Inc., 2023. http://dx.doi.org/10.61700/vksf9usteps6f469.

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Streszczenie:

This seminar provides a hands-on exploration of time-series methods useful for econometrics as well as social and health science research. Any modelling exercise involving time series data depends crucially on the correct treatment of any non-stationarity which may be present in the data. The seminar explores the developments in dynamic modelling and non-stationarity which have taken place over the last 50 years in Econometrics, including in-depth coverage types of non-stationarity and tests for them, including cointegrated relationships (shared trends) among multiple variables. A free version of the EViews software can be. An Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.

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Hall, Stephen. Time-Series Methods: Dynamic Modeling, Non-Stationarity, and Cointegration. Instats Inc., 2022. http://dx.doi.org/10.61700/nyrm5o8t47qqa469.

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Streszczenie:

This seminar provides a hands-on exploration of time-series methods useful for econometrics as well as social and health science research. Any modelling exercise involving time series data depends crucially on the correct treatment of any non-stationarity which may be present in the data. The seminar explores the developments in dynamic modelling and non-stationarity which have taken place over the last 50 years in Econometrics, including in-depth coverage types of non-stationarity and tests for them, including cointegrated relationships (shared trends) among multiple variables. A free version of the EViews software can be [url=https://register1.eviews.com/demo/][u]downloaded here[/u][/url]. An Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.

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Koide, Reo, and Takuya Yoshimura. Dynamic Modelling and Sensitivity Analysis of Automobile Suspension Systems by Application of MBD. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0177.

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Baker, Donald, Avishalom Marani, and D. D. Davis. The Dynamic Aspects of Heterosis in Interspecific Crosses of Cotton-a Plant Modelling Approach. United States Department of Agriculture, January 1988. http://dx.doi.org/10.32747/1988.7592661.bard.

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