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1
UID:
gbv_57043789X
Format: Online Ressource (x, 827 pages) , illustrations.
Edition: Online-Ausg.
ISBN: 0444898573 , 9780444898579
Series Statement: Handbooks in economics 13
Content: The aim of this volume is to provide an introduction and selective overview of the rapidly emerging field of computational economics. Computational economics provides an important set of tools that an increasing number of economists will need to acquire in order to understand and do state-of-the-art research in virtually all areas of economics. Articles in the volume range from very applied, policy oriented applications of computational methods, to highly theoretical and mathematically complex analyses of algorithms and numerical methods. The book emphasizes the unique contributions of computational methods in economics, and focuses on problems for which well developed solutions are not already available from the literature in operations research, numerical methods, and computer science. As well as covering relatively mature areas in the field, a number of chapters are included which cover more speculative "frontier topics", in particular recently discovered computational innovations and research results
Content: The aim of this volume is to provide an introduction and selective overview of the rapidly emerging field of computational economics. Computational economics provides an important set of tools that an increasing number of economists will need to acquire in order to understand and do state-of-the-art research in virtually all areas of economics. Articles in the volume range from very applied, policy oriented applications of computational methods, to highly theoretical and mathematically complex analyses of algorithms and numerical methods. The book emphasizes the unique contributions of computational methods in economics, and focuses on problems for which well developed solutions are not already available from the literature in operations research, numerical methods, and computer science. As well as covering relatively mature areas in the field, a number of chapters are included which cover more speculative "frontier topics", in particular recently discovered computational innovations and research results
Note: Includes bibliographical references and index , Preface. Economic Topics. Computable general equilibrium modelling for policy analysis and forecasting (P.B. Dixon, B.R. Parmenter). Computation of equilibria in finite games (R.D. McKelvey, A. McLennan). Computational methods for macroeconomic models (R.C. Fair). Mechanics of forming and estimating dynamic linear economies (E.W. Andersen et al.). Nonlinear pricing and mechanism design (R. Wilson). Sectoral economics (D.A. Kendrick). Computer Science Parallel computation (A. Nagurney). Artificial intelligence in economics and finance: A state of the art - 1994 (L.F. Pau, P.-Y. Tan). Neural networks for encoding and adapting in dynamic economies (I.-K. Cho, T.J. Sargent). Modelling languages in computational economics: GAMS (S.A. Zenios). Numerical Methods. Mathematica for economists (H. Varian). Approximation, perturbation, and projection methods in economic analysis (K.L. Judd). numerical methods for linear-quadratic models (H.M. Amman). Numerical dynamic programming in economics (J. Rust). Monte Carlo simulation and numerical integration (J. Geweke).
Additional Edition: Erscheint auch als Druck-Ausgabe HT007213756 Handbook of computational economics
Language: English
Subjects: Economics
RVK:
Keywords: Wirtschaftswissenschaften ; Computersimulation ; Datenverarbeitung ; Wirtschaftsinformatik ; Wirtschaftswissenschaften ; Ökonometrie ; Wirtschaftsinformatik ; Electronic books
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Associated Volumes
  • 2
    UID:
    gbv_1831640554
    ISBN: 0444898573
    Content: This chapter describes computable general equilibrium (CGE) modeling and the history of its development. The chapter illustrates the computation of solutions for CGE models and reviews its achievements, failures, and potential. The model illustrated in the chapter can be used in two ways: as a single-period model suitable for comparativestatic analyses; and as a model for multi-period forecasting. Before CGE models, there were inputoutput models that emphasized inputoutput linkages among industries. CGE models go beyond inputoutput models by linking industries via economy-wide constraints including constraints on the size of government budget deficits; constraints on deficits in the balance of trade; constraints on the availability of labor, capital, and land; and constraints arising from environmental considerations, such as air and water quality. Much of CGE modeling has been concerned with the welfare implications of proposed policy changesfor example, changes in protection, changes in taxes, and changes in environmental regulations. Many interesting welfare results have been obtained, especially in the analysis of tax changes.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 3-85, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:3-85
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 3
    UID:
    gbv_1831640465
    ISBN: 0444898573
    Content: A GAMS model is a collection of statements in the GAMS language that define the variables of the model, specify the symbolic relationships among them in the form of equations, specify data structures and assign values to them, and instructs the computer to generate and solve the model. This chapter provides an introduction to algebraic modeling language, general algebraic modeling system (GAMS) of Brooke, Kendrick and Meeraus (1992). The chapter also gives an overview of the language and illustrates its use in modeling problems in transportation, asset allocation, and the estimation of social accounting matrices. This system provides a high-level (algebraic) language for the representation of large and complex models. It allows for unambiguous statements of algebraic relations that define an abstract system of variables and equations. It also provides mechanisms for data management. The system performs appropriate data transformations to create a specific instance of the model, starting from the abstract representation. Because, the model description is algebraic the GAMS statement of the model provides a readable documentation. The data management mechanisms also facilitate the preparation of reports.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 471-488, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:471-488
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 4
    UID:
    gbv_1831640457
    ISBN: 0444898573
    Content: This chapter discusses the design of mathematica, the front end, programming, packages, mathsource, and application in economics. Mathematica is a computer program that can help in mathematics. It can be used to do symbolic, numeric, and graphical analysis. There are two parts to the Mathematica program: the kernel and the front end. The kernel is the basic computational engine and is more-or-less platform independent; the front-end is slightly different for each platform. These two programs can be run separately: the front end can run on a Macintosh while the kernel executes on a remote workstation or a supercomputer. Mathematica keeps a record of a session in a format known as a Notebook. This is an ASCII file and is essentially machine independent. It allows the input and output of Mathematica to be organized in a convenient way. Mathematica also has tools for procedural programming, which is the style of programming used in Fortran, Pascal, and C. However, these toolsDO loops, WHILE loops, and the likeare normally not the best way to program in Mathematica.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 489-505, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:489-505
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 5
    UID:
    gbv_1831640449
    ISBN: 0444898573
    Content: This chapter examines local and global approximation methods that have been used or have potential future value in economic and econometric analysis. The chapter presents the related projection method for solving operator equations and illustrates its application to dynamic economic analysis, dynamic games, and asset market equilibrium with asymmetric information. In the chapter, it is shown that a general class of techniques from the numerical partial differential equations literature can be usefully applied and adapted to solve nonlinear economic problems. Despite the specificity of the applications discussed in the chapter the general description makes clear the general usefulness of perturbation and projection methods for economic problems, both theoretical modeling and empirical analysis. The application of perturbation and projection methods and the underlying approximation ideas have substantially improved the efficiency of economic computations. In addition, exploitation of these ideas will surely lead to progress.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 509-585, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:509-585
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 6
    UID:
    gbv_1831640430
    ISBN: 0444898573
    Content: This chapter discusses the widely used control model in economics, the Linear Quadratic Control Model (LQCM).The major advantage of LQCM is that it has, in general, a unique analytical solution. However, this is not necessarily true when dealing with forward-looking behavior or stochastic models. The same holds for most nonlinear dynamic control problems. Therefore, the solution of the optimization problem has to be obtained through an approximation procedure. The stochastic version of the LQCM explicitly models stochastic parameters and provides the opportunity to address the Lucas critique within the control framework. The stochastic version has some major drawbacks that have to be worked upon.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 587-618, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:587-618
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 7
    UID:
    gbv_1831640422
    ISBN: 0444898573
    Content: This chapter explores the numerical methods for solving dynamic programming (DP) problems. The DP framework has been extensively used in economics because it is sufficiently rich to model almost any problem involving sequential decision making over time and under uncertainty. The chapter focuses on continuous Markov decision processes (MDPs) because these problems arise frequently in economic applications. Although, complexity theory suggests a number of useful algorithms, the theory has relatively little to say about important practical issues, such as determining the point at which various exponential-time algorithms such as Chebyshev approximation methods start to blow up, making it optimal to switch to polynomial-time algorithms. In future work, it will be essential to provide numerical comparisons of a broader range of methods over a broader range of test problems, including problems of moderate to high dimensionality.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 619-729, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:619-729
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 8
    UID:
    gbv_1831640414
    ISBN: 0444898573
    Content: This chapter discusses simulation methods that are both important and useful in the solution of integration problems, and discusses the principles for the practical application of simulation in economics with a focus on integration problems. The simulation methods are generally straightforward for the investigator to implement, relying on an understanding of a few principles of simulation and the structure of the problem at hand. By contrast, deterministic methods require much larger problem-specific investments in numerical methods. Simulation methods can provide solutions for two related integration problems. One integration problem arises in model solution for agents whose expected utilities cannot be expressed as a closed function of state and decision variables. The other occurs, when the investigator combines sources of uncertainty about models to draw conclusions about policy. Markov chain Monte Carlo methods, which make use of samples that are neither independently nor identically distributed, have greatly expanded the scope of integration problems with convenient practical solutions.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 731-800, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:731-800
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 9
    UID:
    gbv_1831640546
    ISBN: 0444898573
    Content: This chapter provides an overview of the latest state of the art of methods for numerical computation of Nash equilibria and refinements of Nash equilibria for general finite n -person games. The appropriate method for computing Nash equilibria for a game depends on a number of factors. The first and most important factor involves, whether it is required to simply find one equilibrium (a sample equilibrium), or find all equilibria. The problem of finding one equilibrium is a well studied problem, and there exist number of different methods for numerically computing a sample equilibrium. The problem of finding all equilibria has been addressed recently. While, there exist methods for computation of all equilibria, they are computationally intensive. With current methods, they are only feasible on small problems. The chapter overviews methods for computing sample equilibria in normal form games, and discusses the computation of equilibria on extensive form games.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 87-142, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:87-142
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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  • 10
    UID:
    gbv_1831640538
    ISBN: 0444898573
    Content: This chapter discusses computational methods for the estimation and analysis of macroeconometric models. The chapter focuses on methods that, while possibly computationally routine, are not trivial. Most of the methods discussed are methods for complete models. The results reported in Fair and Taylor (1990) using the methods discussed in this chapter are encouraging regarding the use of models with rational expectations. FIML estimation seems computationally feasible using the procedure of computing derivatives, for the expectations and stochastic simulation , when done in the manner described in the chapter. FIML estimation is particularly important, because it takes into account all the nonlinear restrictions implied by the rational expectations hypothesis. It is hoped that the methods discussed in this chapter will open the way for many more tests of models with rational expectations.
    In: Handbook of computational economics, Amsterdam : Elsevier, 1996, (1996), Seite 143-169, 0444898573
    In: 9780444898579
    In: year:1996
    In: pages:143-169
    Language: English
    URL: Volltext  (Deutschlandweit zugänglich)
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