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Classical econometrics - which plunges its roots in economic theory with simultaneous equations models (SEM) as offshoots - and time series econometrics - which stems from economic data with vector autoregr- sive (VAR) models as offsprings - scour, like the Janus's facing heads, the flowing of economic variables so as to bring to the fore their autonomous and non-autonomous dynamics. It is up to the so-called final form of a dy namic SEM, on the one hand, and to the so-called representation theorems of (unit-root) VAR models, on the other, to provide informative closed form expressions for the trajectories, or time paths, of the economic vari ables of interest. Should we look at the issues just put forward from a mathematical standpoint, the emblematic models of both classical and time series econometrics would turn out to be difference equation systems with ad hoc characteristics, whose solutions are attained via a final form or a represen tation theorem approach. The final form solution - algebraic technicalities apart - arises in the wake of classical difference equation theory, display ing besides a transitory autonomous component, an exogenous one along with a stochastic nuisance term. This follows from a properly defined ma trix function inversion admitting a Taylor expansion in the lag operator be cause of the assumptions regarding the roots of a determinant equation pe culiar to SEM specifications.
From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.
This book presents the technical aspects of an economic model used to examine issues of global economic significance, such as the impact on the world economy of changes in trade and environmental policy. The book provides a number of studies using the model to examine trade reform, growth and investment, climate change, natural resources, technology, and demographic change and migration.
Modelling is a tool used by savvy business managers to understand the processes of their business and to estimate the impact of changes. Dynamic Modelling for Business Management applies dynamic modelling to business management, using accessible modelling techniques that are demonstrated starting with fundamental processes and advancing to more complex business models. Discussions of modelling emphasize its practical use for decision making and implementing change for measurable results. Readers will learn about both manufacturing and service-oriented business processes using hands-on lessons. Then will then be able to manipulate additional models to try out their knowledge and address issues specific to their own businesses and interests. Some of the topics covered include workflow management, supply-chain-management, and strategy.
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
In this book Christian Gourieroux and Alain Monfort provide an up-to-date and comprehensive analysis of modern time series econometrics. They have succeeded in synthesising in an organised and integrated way a broad and diverse literature. While the book does not assume a deep knowledge of economics, one of its most attractive features is the close attention it pays to economic models and phenomena throughout. The coverage represents a major reference tool for graduate students, researchers and applied economists. The book is divided into four sections. Section one gives a detailed treatment of classical seasonal adjustment or smoothing methods. Section two provides a thorough coverage of various mathematical tools. Section three is the heart of the book, and is devoted to a range of important topics including causality, exogeneity shocks, multipliers, cointegration and fractionally integrated models. The final section describes the main contribution of filtering and smoothing theory to time series econometric problems.
This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap.
A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples. Simulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox. The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises. Contributors Wenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gönenç Yücel
Topics in Modal Analysis II, Volume 6: Proceedings of the 30th IMAC, A Conference and Exposition on Structural Dynamics, 2012, is the sixth volume of six from the Conference and brings together 65 contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Dynamics, including papers on: Aerospace, Acoustics, Energy Harvesting, Shock and Vibration, Finite Element, Structural Health Monitoring, Biodynamics Experimental Techniques, Damage Detection, Rotating Machinery, Sports Equipment Dynamics, Aircraft/Aerospace.
Dynamic Analysis of Structures reflects the latest application of structural dynamics theory to produce more optimal and economical structural designs. Written by an author with over 37 years of researching, teaching and writing experience, this reference introduces complex structural dynamics concepts in a user-friendly manner. The author includes carefully worked-out examples which are solved utilizing more recent numerical methods. These examples pave the way to more accurately simulate the behavior of various types of structures. The essential topics covered include principles of structural dynamics applied to particles, rigid and deformable bodies, thus enabling the formulation of equations for the motion of any structure. - Covers the tools and techniques needed to build realistic modeling of actual structures under dynamic loads - Provides the methods to formulate the equations of motion of any structure, no matter how complex it is, once the dynamic model has been adopted - Provides carefully worked-out examples that are solved using recent numerical methods