Last edited by Faujas
Wednesday, April 22, 2020 | History

6 edition of Optimization heuristics in econometrics found in the catalog.

Optimization heuristics in econometrics

applications of threshold accepting

by Peter Winker

  • 284 Want to read
  • 31 Currently reading

Published by Wiley in Chichester, New York .
Written in English

    Subjects:
  • Econometrics.,
  • Mathematical optimization.

  • Edition Notes

    Includes bibliographical references and indexes.

    Other titlesApplications of threshold accepting
    StatementPeter Winker.
    SeriesWiley series in probability and statistics
    Classifications
    LC ClassificationsHB139 .W565 2001
    The Physical Object
    Paginationxiii, 333 p. :
    Number of Pages333
    ID Numbers
    Open LibraryOL3968262M
    ISBN 100471856312
    LC Control Number2001274389
    OCLC/WorldCa45684061

    Global optimization. A more general question that came up: this page is about global optimization.I propose to merge Optimization (mathematics) and global optimization and store it in global entry Optimization (mathematics) should be changed to give links to global optimization and local optimization and describe the difference (like NP . Mathematical optimization (alternatively spelt optimisation) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods .


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Optimization heuristics in econometrics by Peter Winker Download PDF EPUB FB2

Those researching and working in econometrics, statistics and operations research are given the tools to apply optimization heuristic methods to real problems in their work. Postgraduate students of statistics and econometrics will find the book provides a good introduction to optimization heuristic by: Many problems in statistics and econometrics offer themselves naturally to the use of optimization heuristics.

Standard methods applied to highly complex problems often produce approximate results, of unknown quality, based on heavy assumptions. Heuristic Optimization Methods in Econometrics Manfred Gilli Department of Econometrics, University of Geneva and Swiss Finance Institute, Bd du Cited by: optimization heuristics, the present contribution will provide an extended analy- sis on the implications of the additional stochastic component resulting from the use of heuristics for econometric analysis in Section 3.

Request PDF | On Jan 1,Peter Winker and others published Optimization Heuristics in Econometrics: Applications of Threshold Accepting | Find, Author: Peter Winker.

Those researching and working in econometrics, statistics and operations research are given the tools to apply optimization heuristic methods to real problems in their work.

Postgraduate students of statistics and econometrics will find the book provides a good introduction to optimization heuristic methods. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics.

This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested : Paperback. A review of heuristic optimization methods in econometrics⋆ Manfred Gillib, Peter Winkera,∗, aDepartment of Economics, University of Giessen, Germany bDepartment of Econometrics, University of Geneva and Swiss Finance Institute Abstract Estimation and modelling problems as they arise in many fields often turn out toCited by: Overview of optimization heuristics Two broad classes: † Construction methods (greedy algorithms) † Local search methods Solution space not explored systematically A particular heuristic is characterized by the way the walk through the solution domain is organized Optimization heuristics 13File Size: 1MB.

Summary: “Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization.

Global and combinatorial optimization heuristics are widely used in different areas ranging from engineering to operational research. This introduction to the fast growing field of optimization heuristics offers the knowledge to use the techniques in a number of different application areas.

For the heuristic search step, we consider two parsimonious but effective algorithms which can be easily implemented by non-specialists in heuristic optimization: the differential evolution (DE.

This book is an introduction to the field of hyper-heuristics illustrating their application. The book will be of value to researchers, graduate students, and practitioners in the areas of biologically inspired computing, optimization, and operations research.

Applied Probability and Statistics Section)|Optimization Heuristics in Econometrics: Applications Using Threshold Accepting (Wiley Series in Probability and Statistics. Applied Probability and Statistics Section) (any file),Optimization Heuristics in Econometrics: Applications Using Threshold Accepting (Wiley Series in Probability and Statistics.

Economics textbooks. Mike Moffatt, Ph.D., is an economist and professor. He teaches at the Richard Ivey School of Business and serves as a research fellow at the Lawrence National Centre for Policy and Management.

Q: If I want to achieve a Ph.D. in economics what steps would you advise me to take and what books and courses would I need to study Author: Mike Moffatt. Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems.

Editor(s): Kwang Y. Lee; Mohamed A. El‐Sharkawi; This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems.

It begins with an overview of modern heuristic techniques and. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

Downloadable. Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying.

A different approach consists in applying optimization heuristics such as Cited by:   A Review of Heuristic Optimization Methods in Econometrics. Swiss Finance Institute Research Paper No.

Goffe, W. L., G. Ferrier and J. Rogers (). Global optimization of statistical functions with simulated annealing. Journal of Econometr McCullough, B. and H. Vinod (). The Numerical Reliability of Econometric. Optimization in Economics and Finance: Some Advances in Non-Linear, Dynamic, Multi-Criteria and Stochastic Models (Dynamic Modeling and Econometrics in Economics and Finance Book 7) - Kindle edition by Craven, Bruce D., Islam, Sardar M.

Download it once and read it on your Kindle device, PC, phones or by: 2. Although the use of these methods became more standard in several fields of sciences, their use in estimation and modelling in econometrics appears to be still limited.

We present an introduction to heuristic optimization methods and provide some examples for which these methods are found to work by: Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics.

This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested cturer: Academic Press.

Summary. Linear regression is widely-used in finance. While the standard method to obtain parameter estimates, Least Squares, has very appealing theoretical and numerical properties, obtained estimates are often unstable in the presence of extreme observations which are rather common in financial time by: 6.

Format: HardcoverVerified Purchase. This is an excellent real analysis book with a lot of material that fits perfectly any one's interests in economic theory. Other real analysis books out there do not cover things that are very important in economics, e.g, fix Cited by: This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

It is rather a “different courses, different horses” situation where criteria such as the type of optimization problem, restrictions on computational time, experience with implementing different HO algorithms, the programming environment, the availability of toolboxes, and so on that influence the decision which heuristic to choose - or.

He has written on numerical methods and their application in finance, with a focus on asset allocation. His research interests include quantitative investment strategies and portfolio. Request PDF | Applications of Heuristics in Finance | Having the optimal solution for a given problem is crucial in the competitive world of finance; finding this optimal solution, however, is.

Downloadable. Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods.

One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as.

Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics.

This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically. A global optimization heuristic for portfolio choice with VaR and expected shortfall.

In: Computational Methods in Decision-making, Economics and Finance (P. Pardalos and D.W. Hearn, Ed.). Applied Optimization by: Gilli M., Këllezi E.

() A Global Optimization Heuristic for Portfolio Choice with VaR and Expected Shortfall. In: Kontoghiorghes E.J., Rustem B., Siokos S. (eds) Computational Methods in Decision-Making, Economics and by: In computer science, artificial intelligence, and mathematical optimization, a heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution.

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Many optimization questions arise in economics and finance; an important example of this is the society's choice of the optimum state of the economy (the social choice problem).

Optimization in Economics and Finance extends and improves the usual optimization techniques, in a form that may be adopted for modeling social choice problems. The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this Methodologies has been expanded and brought.

A formal framework for joint convergence analysis of both, the estimators and the heuristic, has been previously described within the context of the GARCH(1,1) model. The aim of this contribution is to adapt and extend this research to asymmetric and Author: Alexandru Mandes, Cristian Gatu, Peter Winker.

Heuristics are simple strategies or mental processes that humans, animals, organizations and machines use to quickly form judgments, make decisions, and find solutions to complex problems.

This happens when an individual focuses on the most relevant aspects of a problem or situation to formulate a solution. It discusses topics such as hyper-heuristics algorithmic enhancements and performance measurement approaches, and provides insights into the implementation of meta-heuristic strategies to multi-objectives optimization real-life problems in business, economics and finance.

With this book, readers can learn to solve real-world sustainable. The book mainly concentrates on approximate, randomized and heuristic algorithms, and on the theoretical and experimental comparison of these approaches according to the requirements of the practice.

There exist several monographs specializing in some of these methods, but no book systematically explains and compares all main possibilities of Brand: Springer-Verlag Berlin Heidelberg.

Cover artfor the second print edition is a time plot of the paths of particles in Particle Swarm Optimization working their way towards the optimum of the Rastrigin problem. This document is was produced in part via National Science Foundation grants and In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.Books shelved as mathematical-economics: Fundamental Methods of Mathematical Economics by Alpha C.

Chiang, Schaum's Outline of Mathematical Economics by.