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"General-equilibrium" refers to an analytical approach which looks at the economy as a complete system of inter-dependent components (industries, households, investors, governments, importers and exporters). "Applied" means that the primary interest is in systems that can be used to provide quantitative analysis of economic policy problems in particular countries. Reflecting the authors' belief in the models as vehicles for practical policy analysis, a considerable amount of material on data and solution techniques as well as on theoretical structures has been included. The sequence of chapters follows what is seen as the historical development of the subject.
The book is directed at graduate students and professional economists who may have an interest in constructing or applying general equilibrium models. The exercises and readings in the book provide a comprehensive introduction to applied general equilibrium modeling. To enable the reader to acquire hands-on experience with computer implementations of the models which are described in the book, a companion set of diskettes is available.
Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models. This book innovatively: *Develops procedures for studying social, economic, and human development. * Uses typologies to group (i.e., classify or nest) the level of random macro-level factors. * Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure. * Selects appropriate covariance structures for generalized linear mixed models. * Applies difference-in-differences study designs in the multilevel modeling of intervention studies. *Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data. * Uses the Kenward-Rogers correction in mixed models of repeated measures. * Explicates differences between associational and causal analysis of multilevel models. * Consolidates research findings via meta-analysis and methodological critique. *Develops criteria for assessing a study's validity and zone of causality. Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.
Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search. Drawing upon years of academic research and using numerous examples and illustrative applications, the authors, Ling Zhang and Bo Zhang provide a unique guide to computerized problem solving and granular computing. This book is a valuable guide to graduate students, research fellows, and academics specializing in artificial intelligence or concerned with computerized problem solving and granular computing. It explains the theory of hierarchical problem solving, its computational complexity, and discusses the principle and applications of multi-granular computing. It describes a human-like, theoretical framework using quotient space theory, that will be of interest to researchers in artificial intelligence. It provides many applications and examples in the engineering and computer science area. It includes complete coverage of planning, heuristic search and coverage of strictly mathematical models.
Semiotics is the science of signs: graphical, such as pictures; verbal (writing or sounds); or others such as body gestures and clothes. Computer semiotics studies the special nature of computer-based signs and how they function in use. This 1991 book is based on ten years of empirical research on computer usage in work situations and contains material from a course taught by the author. It introduces basic traditional semiotic concepts and adapts them so that they become useful for analysing and designing computer systems in their symbolic context of work. It presents a novel approach to the subject, rich in examples, in that it is both theoretically systematic and practical. The author refers to and reinterprets techniques already used so that readers can deepen their understanding. In addition, it offers new techniques and a consistent perspective on computer systems that is particularly appropriate for new hardware and software (e.g. hypermedia) whose main functions are presentation and communication. This is a highly important work whose influence will be wide and longlasting.
This Research Note presents some recent advances in various important domains of partial differential
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