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| Author | : |
| Publisher | : Springer |
| Total Pages | : 596 |
| Release | : 2012-08-31 |
| Genre | : |
| ISBN | : 9781461416548 |
| Author | : Xiaohong Chen |
| Publisher | : |
| Total Pages | : 234 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
| Author | : Xiaohong Chen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 582 |
| Release | : 2012-08-01 |
| Genre | : Business & Economics |
| ISBN | : 1461416531 |
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
| Author | : El Hassan Zerrik |
| Publisher | : Springer Nature |
| Total Pages | : 275 |
| Release | : 2019-08-26 |
| Genre | : Technology & Engineering |
| ISBN | : 3030261492 |
This book describes recent developments in a wide range of areas, including the modeling, analysis and control of dynamical systems, and explores related applications. The book provided a forum where researchers have shared their ideas, results on theory, and experiments in application problems. The current literature devoted to dynamical systems is quite large, and the authors’ choice for the considered topics was motivated by the following considerations. Firstly, the mathematical jargon for systems theory remains quite complex and the authors feel strongly that they have to maintain connections between the people of this research field. Secondly, dynamical systems cover a wider range of applications, including engineering, life sciences and environment. The authors consider that the book is an important contribution to the state of the art in the fuzzy and dynamical systems areas.
| Author | : Yoon-Jae Whang |
| Publisher | : Cambridge University Press |
| Total Pages | : 279 |
| Release | : 2019-01-31 |
| Genre | : Business & Economics |
| ISBN | : 1108690475 |
This book offers an up-to-date, comprehensive coverage of stochastic dominance and its related concepts in a unified framework. A method for ordering probability distributions, stochastic dominance has grown in importance recently as a way to measure comparisons in welfare economics, inequality studies, health economics, insurance wages, and trade patterns. Whang pays particular attention to inferential methods and applications, citing and summarizing various empirical studies in order to relate the econometric methods with real applications and using computer codes to enable the practical implementation of these methods. Intuitive explanations throughout the book ensure that readers understand the basic technical tools of stochastic dominance.
| Author | : Dimitris N. Politis |
| Publisher | : Springer |
| Total Pages | : 256 |
| Release | : 2015-11-13 |
| Genre | : Mathematics |
| ISBN | : 3319213474 |
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation freed practitioners from the limitations of parametric models, and paved the way towards the `big data' era of the 21st century. Nonetheless, there is a further step one may take, i.e., going beyond even nonparametric models; this is where the Model-Free Prediction Principle is useful. Interestingly, being able to predict a response variable Y associated with a regressor variable X taking on any possible value seems to inadvertently also achieve the main goal of modeling, i.e., trying to describe how Y depends on X. Hence, as prediction can be treated as a by-product of model-fitting, key estimation problems can be addressed as a by-product of being able to perform prediction. In other words, a practitioner can use Model-Free Prediction ideas in order to additionally obtain point estimates and confidence intervals for relevant parameters leading to an alternative, transformation-based approach to statistical inference.
| Author | : Subhash C. Ray |
| Publisher | : Springer Nature |
| Total Pages | : 1797 |
| Release | : 2022-06-02 |
| Genre | : Business & Economics |
| ISBN | : 9811034559 |
This three-volume handbook includes state-of-the-art surveys in different areas of neoclassical production economics. Volumes 1 and 2 cover theoretical and methodological issues only. Volume 3 includes surveys of empirical applications in different areas like manufacturing, agriculture, banking, energy and environment, and so forth.
| Author | : Shahla Seifi |
| Publisher | : Springer Nature |
| Total Pages | : 205 |
| Release | : 2021-10-01 |
| Genre | : Business & Economics |
| ISBN | : 981336498X |
This book focuses mainly on strategic decision making at a global level, which is rarely considered in approaches to sustainability. This book makes a unique contribution as the work looks at global consequences of mineral exhaustion and steps that can be taken to alleviate the impending problems. This book highlights how sustainability has become one of the most important issues for businesses, governments and society at large. This book explores the topic of sustainability as one that is under much debate as to what it actually is and how it can be achieved, but it is completely evident that the resources of the planet are fixed in quantity, and once used, cannot be reused except through being reused in one form or another. This is particularly true of the mineral resources of the planet. These are finite in quantity, and once fully extracted, extra quantities are no longer available for future use. This book argues and presents evidence that the remaining mineral resources are diminishing significantly and heading towards exhaustion. Once mined and consumed, they are no longer available for future use other than what can be recycled and reused. This book demonstrates that future scarcity means that best use must be made of what exists, as sustainability depends upon this, and best use is defined as utility rather than economic value, which must be considered at a global level rather than a national level. Moreover, sustainability depends upon both availability in the present and in the future, so the use of resources requires attention to the future as well as to the present. This book investigates the alternative methods of achieving the global distribution of these mineral resources and proposes an optimum solution. This book adds to the discourse through the understanding of the importance of the depletion and finiteness of raw materials and their use for the present and the future, in order to achieve and maintain sustainability.
| Author | : David F. Hendry |
| Publisher | : MIT Press |
| Total Pages | : 387 |
| Release | : 2014-07-04 |
| Genre | : Business & Economics |
| ISBN | : 0262324423 |
A synthesis of the authors' groundbreaking econometric research on automatic model selection, which uses powerful computational algorithms and theory evaluation. Economic models of empirical phenomena are developed for a variety of reasons, the most obvious of which is the numerical characterization of available evidence, in a suitably parsimonious form. Another is to test a theory, or evaluate it against the evidence; still another is to forecast future outcomes. Building such models involves a multitude of decisions, and the large number of features that need to be taken into account can overwhelm the researcher. Automatic model selection, which draws on recent advances in computation and search algorithms, can create, and then empirically investigate, a vastly wider range of possibilities than even the greatest expert. In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection. After introducing the principles of empirical model discovery and the role of model selection, Hendry and Doornik outline the stages of developing a viable model of a complicated evolving process. They discuss the discovery stages in detail, considering both the theory of model selection and the performance of several algorithms. They describe extensions to tackling outliers and multiple breaks, leading to the general case of more candidate variables than observations. Finally, they briefly consider selecting models specifically for forecasting.
| Author | : Gail L. Cramer |
| Publisher | : Routledge |
| Total Pages | : 806 |
| Release | : 2018-07-17 |
| Genre | : Business & Economics |
| ISBN | : 1317225767 |
This Handbook offers an up-to-date collection of research on agricultural economics. Drawing together scholarship from experts at the top of their profession and from around the world, this collection provides new insights into the area of agricultural economics. The Routledge Handbook of Agricultural Economics explores a broad variety of topics including welfare economics, econometrics, agribusiness, and consumer economics. This wide range reflects the way in which agricultural economics encompasses a large sector of any economy, and the chapters present both an introduction to the subjects as well as the methodology, statistical background, and operations research techniques needed to solve practical economic problems. In addition, food economics is given a special focus in the Handbook due to the recent emphasis on health and feeding the world population a quality diet. Furthermore, through examining these diverse topics, the authors seek to provide some indication of the direction of research in these areas and where future research endeavors may be productive. Acting as a comprehensive, up-to-date, and definitive work of reference, this Handbook will be of use to researchers, faculty, and graduate students looking to deepen their understanding of agricultural economics, agribusiness, and applied economics, and the interrelationship of those areas.
