Experiments with Input-output Models
Author: A. Ghosh
Publisher: CUP Archive
Published: 1964
Total Pages: 182
ISBN-13:
DOWNLOAD EBOOKPosts
Simulating the Urban Economy
Author: P. Smith
Publisher: Routledge
Published: 2012-12-06
Total Pages: 163
ISBN-13: 1135678286
DOWNLOAD EBOOKFirst published in 1974. The demand for input-output models at the urban and regional levels is growing rapidly, as planners and government officers are becoming increasingly aware of the value and potential of the approach in subnational studies. Input-output models provide a basis for the detailed study of the economic system, emphasising clearly the interrelationships present in an economy. The present study attempts to integrate previous work aimed at the production of non-survey input-output tables at the regional level.
Input-output Economics
Author: Wassily Leontief
Publisher: Oxford University Press, USA
Published: 1986
Total Pages: 449
ISBN-13: 0195035275
DOWNLOAD EBOOKThis collection of writings provides the only comprehensive introduction to the input-output model for which Leontief was awarded the Nobel Prize in 1973. The structural approach to economics developed by Leontief, and known as input-output analysis, paved the way for the transformation of economics into a truly empirical discipline that could utilize modern data processing technology. This thoroughly revised second edition includes twenty essays--twelve of which are new to this edition--that reflect the past developments and the present state of the field. Beginning with an introductory chapter, the book leads the reader into an understanding of the input-output approach--not only as formal theory but also as a research strategy and powerful tool for dealing with a complex modern economy.
Economic Effects of Natural Disasters
Author: Taha Chaiechi
Publisher: Academic Press
Published: 2020-10-16
Total Pages: 665
ISBN-13: 0128174668
DOWNLOAD EBOOKEconomic Effects of Natural Disasters explores how natural disasters affect sources of economic growth and development. Using theoretical econometrics and real-world data, and drawing on advances in climate change economics, the book shows scholars and researchers how to use various research methods and techniques to investigate and respond to natural disasters. No other book presents empirical frameworks for the evaluation of the quality of macroeconomic research practice with a focus on climate change and natural disasters. Because many of these subjects are so large, different regions of the world use different approaches, hence this resource presents tailored economic applications and evidence. - Connects economic theories and empirical work in climate change to natural disaster research - Shows how advances in climate change and natural disaster research can be implemented in micro- and macroeconomic simulation models - Addresses structural changes in countries afflicted by climate change and natural disasters
Handbook of Input-Output Economics in Industrial Ecology
Author: Sangwon Suh
Publisher: Springer Science & Business Media
Published: 2009-05-13
Total Pages: 885
ISBN-13: 1402057377
DOWNLOAD EBOOKIndustrial Ecology (IE) is an emerging multidisciplinary field. University departments and higher education programs are being formed on the subject following the lead of Yale University, The Norwegian University of Science and Technology (NTNU), Leiden University, University of Michigan at Ann Arbor, Carnegie Mellon University, University of California at Berkeley, Institute for Superior Technology in Lisbon, Eidgenössische Technische Hochschule (ETH) Zürich, and The University of Tokyo. IE deals with stocks and flows in interconnected networks of industry and the environment, which relies on a basic framework for analysis. Among others, Input-Output Analysis (IOA) is recognized as a key conceptual and analytical framework for IE. A major challenge is that the field of IOA manifests a long history since the 1930s with two Nobel Prize Laureates in the field and requires considerable analytical rigor. This led many instructors and researchers to call for a high-quality publication on the subject which embraces both state-of-the-art theory and principles as well as practical applications.
Estimation of stochastic input-output models
Author: S.D. Gerking
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 98
ISBN-13: 1461343623
DOWNLOAD EBOOKThis monograph is a revision of my Indiana University doctoral disserta tion which was completed in April, 1975. Thanks are, therefore, due to the members of my doctoral committee: Saul Pleeter (Chairman), David J. Behling, R. Jeffery Green, Richard L. Pfister, and Elmus Wicker for their helpful comments on previous versions of the manuscript. In addition, I am indebted to the Division of Research and to the Office of Research and Advanced Studies at Indiana University for financial support. As the reader will observe, the techniques developed in Chapters 3 and 4 of this monograph are illustrated using input-output data from West Virginia. These data were generously made available by William H. Miernyk, Director of the Regional Research Institute at West Virginia University. I also wish to acknowledge the Bureau of Business and Eco nomic Research at Arizona State University for providing two research assistants, Kevin A. Nosbisch and Tom R. Rex, who aided in processing the West Virginia data. A third research assistant, Phillip M. Cano, also worked on this project as part of an independent study program taken under my direction during the spring semester of 1975. Finally, I must thank Mary Holguin and Margaret Shumway who expertly typed the final copy of the manuscript. Despite the efforts of all the individuals mentioned above, I assume responsibility for any errors which may remain.
Microcomputer Based Input-output Modeling
Author: Daniel M. Otto
Publisher: CRC Press
Published: 2019-03-04
Total Pages: 220
ISBN-13: 0429710348
DOWNLOAD EBOOKThis book discusses recent developments in Input-Output (I/O) models for microcomputers and applications of I/O models in regional studies. It provides background information on traditional I/O models and a set of working examples of I/O applications for users.
The Design and Analysis of Computer Experiments
Author: Thomas J. Santner
Publisher: Springer Science & Business Media
Published: 2013-03-09
Total Pages: 288
ISBN-13: 1475737998
DOWNLOAD EBOOKThis book describes methods for designing and analyzing experiments conducted using computer code in lieu of a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment). It also provides techniques for analyzing the resulting data so as to achieve these research goals.
The Design and Analysis of Computer Experiments
Author: Thomas J. Santner
Publisher: Springer
Published: 2019-01-08
Total Pages: 446
ISBN-13: 1493988476
DOWNLOAD EBOOKThis book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: • An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples • A new comparison of plug-in prediction methodologies for real-valued simulator output • An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions • A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization • A new chapter describing graphical and numerical sensitivity analysis tools • Substantial new material on calibration-based prediction and inference for calibration parameters • Lists of software that can be used to fit models discussed in the book to aid practitioners
Stochastic Methods for Parameter Estimation and Design of Experiments in Systems Biology
Author: Andrei Kramer
Publisher: Logos Verlag Berlin GmbH
Published: 2016-02-11
Total Pages: 164
ISBN-13: 3832541950
DOWNLOAD EBOOKMarkov Chain Monte Carlo (MCMC) methods are sampling based techniques, which use random numbers to approximate deterministic but unknown values. They can be used to obtain expected values, estimate parameters or to simply inspect the properties of a non-standard, high dimensional probability distribution. Bayesian analysis of model parameters provides the mathematical foundation for parameter estimation using such probabilistic sampling. The strengths of these stochastic methods are their robustness and relative simplicity even for nonlinear problems with dozens of parameters as well as a built-in uncertainty analysis. Because Bayesian model analysis necessarily involves the notion of prior knowledge, the estimation of unidentifiable parameters can be regularised (by priors) in a straight forward way. This work draws the focus on typical cases in systems biology: relative data, nonlinear ordinary differential equation models and few data points. It also investigates the consequences of parameter estimation from steady state data; consequences such as performance benefits. In biology the data is almost exclusively relative, the raw measurements (e.g. western blot intensities) are normalised by control experiments or a reference value within a series and require the model to do the same when comparing its output to the data. Several sampling algorithms are compared in terms of effective sampling speed and necessary adaptations to relative and steady state data are explained.
