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The paper provides empirical evidence on collection lags in major categories of government revenue and analyzes the estimated revenue-eroding effects of inflation within the standard model of inflationary finance. The evidence indicates a wide variation in collection lags among the categories of revenues. The estimated erosion of real fiscal revenue, although varied in the sample countries, appears to have substantially offset gains from the inflation tax, thereby severely restricting the use of this form of taxation in generating resources.
The observation that collection lags combine with inflation to erode fiscal revenues has long been a strong argument against seigniorage (Tanzi (1978)). However, with the exception of Dixit (1991), who used a general equilibrium model to reject this argument, the optimal tax literature has not analyzed how collection lags affect desired tax structures. In this paper, this issue is re-examined using an overlapping generations version of Dixit’s model. It is shown that depending on the specification of the collection cost function and the size of government spending in GDP, collection lags may increase, leave unchanged, or reduce the desired rate of inflation.
In many biological models it is necessary to allow the rates of change of the variables to depend on the past history, rather than only the current values, of the variables. The models may require discrete lags, with the use of delay-differential equations, or distributed lags, with the use of integro-differential equations. In these lecture notes I discuss the reasons for including lags, especially distributed lags, in biological models. These reasons may be inherent in the system studied, or may be the result of simplifying assumptions made in the model used. I examine some of the techniques available for studying the solution of the equations. A large proportion of the material presented relates to a special method that can be applied to a particular class of distributed lags. This method uses an extended set of ordinary differential equations. I examine the local stability of equilibrium points, and the existence and frequency of periodic solutions. I discuss the qualitative effects of lags, and how these differ according to the choice of discrete or distributed lag. The models studied are drawn from the population dynamiCS of single species (logistic growth, the chemostat) and of interacting pairs of species (predation, mutualism), from cell population dynamiCS (haemopoiesis) and from biochemical kinetics (the Goodwin oscillator). The last chapter is devoted to a population model employing difference equations. All these models include non-linear terms.
A method is presented for determining the control gearing and time lag necessary for determining a specified damping of the motions of an aircraft equipped with an autopilot having a constant-time-lag characteristics. The method is applied to a typical present-day airplane equipped with an autopilot sensitive to yawing acceleration. The types of motion predicted for this airplane-autopilot system by this method are compared with motions calculated by a step-by-step procedure.
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Published in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students. In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide an expanded (in coverage and format), full color illustrated, more comprehensive treatment of the subject with a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of geologic data and concepts, scale considerations, and uncertainty models receive greater attention, and new comprehensive sections are provided on preliminary geological modeling concepts, data inventory, conceptual model, problem formulation, large scale modeling, multiple point-based simulation and event-based modeling. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples with discussion on method capabilities and selection. For example, this expanded second edition includes extensive discussion on the process of moving from an inventory of data and concepts through conceptual model to problem formulation to solve practical reservoir problems. A greater number of examples are included, with a set of practical geostatistical studies developed to illustrate the steps from data analysis and cleaning to post-processing, and ranking. New methods, which have developed in the field since the publication of the first edition, are discussed, such as models for integration of diverse data sources, multiple point-based simulation, event-based simulation, spatial bootstrap and methods to summarize geostatistical realizations.