This book represents an ongoing research agenda the aim of which is to contribute to the Keynesian paradigm in macroeconomics. It examines the Dynamic General Equilibrium (DGE) model, the assumption of intertemporal optimizing behavior of economic agents, competitive markets and price mediated market clearing through flexible wages and prices.
This book gives a comprehensive description of macroeconometric modeling and its development over time. The first part depicts the history of macroeconometric model building, starting with Jan Tinbergen's and Lawrence R. Klein's contributions. It is unique in summarizing the development and specific structure of macroeconometric models built in North America, Europe, and various other parts of the world. The work thus offers an extensive source for researchers in the field. The second part of the book covers the systematic characteristics of macroeconometric models. It includes the household and enterprise sectors, disequilibria, financial flows, and money market sectors.
This book presents a macroeconomic dynamic model à la Solow-Swan, including the market for labor, in a discrete time structure. The model is expanded to include expenditure on R&D and public expenditure on infrastructure. For each of the three models the results are shown in time series figures, which demonstrate that even small changes in the parameters produce responses in the time behavior of the main variables: from steady growth, to regular cycles, to chaotic-like time paths.
Offers an overview of state of the art passive macromodeling techniques with an emphasis on black-box approaches This book offers coverage of developments in linear macromodeling, with a focus on effective, proven methods. After starting with a definition of the fundamental properties that must characterize models of physical systems, the authors discuss several prominent passive macromodeling algorithms for lumped and distributed systems and compare them under accuracy, efficiency, and robustness standpoints. The book includes chapters with standard background material (such as linear time-invariant circuits and systems, basic discretization of field equations, state-space systems), as well as appendices collecting basic facts from linear algebra, optimization templates, and signals and transforms. The text also covers more technical and advanced topics, intended for the specialist, which may be skipped at first reading. Provides coverage of black-box passive macromodeling, an approach developed by the authors Elaborates on main concepts and results in a mathematically precise way using easy-to-understand language Illustrates macromodeling concepts through dedicated examples Includes a comprehensive set of end-of-chapter problems and exercises Passive Macromodeling: Theory and Applications serves as a reference for senior or graduate level courses in electrical engineering programs, and to engineers in the fields of numerical modeling, simulation, design, and optimization of electrical/electronic systems. Stefano Grivet-Talocia, PhD, is an Associate Professor of Circuit Theory at the Politecnico di Torino in Turin, Italy, and President of IdemWorks. Dr. Grivet-Talocia is author of over 150 technical papers published in international journals and conference proceedings. He invented several algorithms in the area of passive macromodeling, making them available through IdemWorks. Bjørn Gustavsen, PhD, is a Chief Research Scientist in Energy Systems at SINTEF Energy Research in Trondheim, Norway. More than ten years ago, Dr. Gustavsen developed the original version of the vector fitting method with Prof. Semlyen at the University of Toronto. The vector fitting method is one of the most widespread approaches for model extraction. Dr. Gustavsen is also an IEEE fellow.
Macrostructures are higher-level semantic or conceptual structures that organize the ‘local’ microstructures of discourse, interaction, and their cognitive processing. They are distinguished from other global structures of a more schematic nature, which we call superstructures. Originally published in 1980, the theory of macrostructures outlined in this book is the result of research carried out during the previous 10 years in the domains of literary theory, text grammar, the general theory of discourse, pragmatics, and the cognitive psychology of discourse processing. The presentation of the theory is systematic but informal and at this stage was not intended to be fully formalized.
Describes macromodelling with SPICE, a circuit simulation program. The book covers the applicability of SPICE macromodelling in education and industry. 31 drop-in models, simulated and verified for use either singly or in groups to perform any analog signal processing function, are provided.
Digital Timing Macromodeling for VLSI Design Verification first of all provides an extensive history of the development of simulation techniques. It presents detailed discussion of the various techniques implemented in circuit, timing, fast-timing, switch-level timing, switch-level, and gate-level simulation. It also discusses mixed-mode simulation and interconnection analysis methods. The review in Chapter 2 gives an understanding of the advantages and disadvantages of the many techniques applied in modern digital macromodels. The book also presents a wide variety of techniques for performing nonlinear macromodeling of digital MOS subcircuits which address a large number of shortcomings in existing digital MOS macromodels. Specifically, the techniques address the device model detail, transistor coupling capacitance, effective channel length modulation, series transistor reduction, effective transconductance, input terminal dependence, gate parasitic capacitance, the body effect, the impact of parasitic RC-interconnects, and the effect of transmission gates. The techniques address major sources of errors in existing macromodeling techniques, which must be addressed if macromodeling is to be accepted in commercial CAD tools by chip designers. The techniques presented in Chapters 4-6 can be implemented in other macromodels, and are demonstrated using the macromodel presented in Chapter 3. The new techniques are validated over an extremely wide range of operating conditions: much wider than has been presented for previous macromodels, thus demonstrating the wide range of applicability of these techniques.
Researchers in the natural sciences are faced with problems that require a novel approach to improve the quality of forecasts of processes that are sensitive to environmental conditions. Nonlinearity of a system may significantly complicate the predictability of future states: a small variation of parameters can dramatically change the dynamics, while sensitive dependence of the initial state may severely limit the predictability horizon. Uncertainties also play a role. This volume addresses such problems by using tools from chaos theory and systems theory, adapted for the analysis of problems in the environmental sciences. Sensitive dependence on the initial state (chaos) and the parameters are analyzed using methods such as Lyapunov exponents and Monte Carlo simulation. Uncertainty in the structure and the values of parameters of a model is studied in relation to processes that depend on the environmental conditions. These methods also apply to biology and economics. For research workers at universities and (semi)governmental institutes for the environment, agriculture, ecology, meteorology and water management, and theoretical economists.
Debt is an important form of financing economic development, especially external debt is in the form of foreign exchange inflows. Exports may not bring in the necessary amount of foreign exchange needed for more imports, or foreign direct investment may not be sufficient for rapid economic development. Debt may bring in benefits/profits or may become a problem of liquidity or solvency. Debt is profitable when its usage brings in discounted streams of rates of return greater than its discounted streams of costs. Illiquidity is a short-run inadequacy of foreign exchange whereas solvency is a long-run problem in the same respect. Debt crisis - a long run solvency problem - refers to a situation where a country or a region undergo rescheduling; i.e. postponement of interest and principal repayments as a result of inability to repay debt. Rescheduling occurs often through the process of negotiations between debtors and creditors. A country can also declare a moratorium which is more severe because it means repayments of interest or both interest and principal are stopped temporarily until creditors agree to negotiate. The 1980s marked a decade where there were developing country-wide debt problem. The nature of debt problem broadly differ among regions. The Latin American countries went into debt crisis due to excessive borrowings in the international credit markets including the Euro-currency market. The debt crisis in the African region predates that of the Latin Americans due to scarcity of foreign exchange earnings via exports. The ASEAN region has lesser debt problems of illiquidity in nature, thus perceived as creditworthy by over-viewers, facilitating more capital inflows in either the form of foreign investment or foreign debt.
Computer-aided full-wave electromagnetic (EM) analysis has been used in microwave engineering for the past decade. Initially, its main application area was design verification. Today, EM-simulation-driven optimization and design closure become increasingly important due to the complexity of microwave structures and increasing demands for accuracy. In many situations, theoretical models of microwave structures can only be used to yield the initial designs that need to be further fine-tuned to meet given performance requirements. In addition, EM-based design is a must for a growing number of microwave devices such as ultra-wideband (UWB) antennas, dielectric resonator antennas and substrate-integrated circuits. For circuits like these, no design-ready theoretical models are available, so design improvement can only be obtained through geometry adjustments based on repetitive, time-consuming simulations. On the other hand, various interactions between microwave devices and their environment, such as feeding structures and housing, must be taken into account, and this is only possible through full-wave EM analysis.Electromagnetic simulations can be highly accurate, but they tend to be computationally expensive. Therefore, practical design optimization methods have to be computationally efficient, so that the number of CPU-intensive high-fidelity EM simulations is reduced as much as possible during the design process. For the same reasons, techniques for creating fast yet accurate models of microwave structures become crucially important.In this edited book, the authors strive to review the state-of-the-art simulation-driven microwave design optimization and modeling. A group of international experts specialized in various aspects of microwave computer-aided design summarize and review a wide range of the latest developments and real-world applications. Topics include conventional and surrogate-based design optimization techniques, methods exploiting adjoint sensitivity, simulation-based tuning, space mapping, and several modeling methodologies, such as artificial neural networks and kriging. Applications and case studies include microwave filters, antennas, substrate integrated structures and various active components and circuits. The book also contains a few introductory chapters highlighting the fundamentals of optimization and modeling, gradient-based and derivative-free algorithms, metaheuristics, and surrogate-based optimization techniques, as well as finite difference and finite element methods./a
