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  • macroeconomics  (2)
  • computational economics.  (1)
  • 1
    Electronic Resource
    Electronic Resource
    What is Computational Economics? (1997)
    Springer
    Computational economics 10 (1997), S. 103-105 
    Details
    ISSN: 1572-9974
    Keywords: computational economics.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract Computational Economics is rapidly evolving into an independent branche in economics. This paper describes the current and future developments within the field of Computational Economics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008661319094
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Teaching Macroeconomics with GAMS (1998)
    Springer
    Computational economics 12 (1998), S. 125-149 
    Details
    ISSN: 1572-9974
    Keywords: computational economics ; macroeconomics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract Our general goal in this paper is to show how to implement in GAMS standard deterministic nonlinear macro models, and stochastic linear macro models with rational expectations. We will also present basic concepts on solution methods and policy analysis for these kinds of models. As a practical illustration, we will use some well known teaching and experimental models in the macroeconomic literature.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008649615981
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  • 3
    Electronic Resource
    Electronic Resource
    Should Macroeconomic Policy Makers Consider Parameter Covariances? (1999)
    Springer
    Computational economics 14 (1999), S. 263-267 
    Details
    ISSN: 1572-9974
    Keywords: macroeconomics ; learning ; stochastic optimization ; numerical experiments
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract Many macroeconomic policy exercises consider the mean values of parameter estimates but do not use the variances and covariances. One can argue that the uncertainty of these parameter estimates is sufficiently small that it can safely be ignored. Or one can take the position that this kind of uncertainty cannot be avoided no matter what one does. Thus it is just as well to ignore it while making policy decisions. In this paper we address both of these positions in the presence of learning and find that they are unconvincing. To the contrary, we find evidence that the potential damage from ignoring the variances and covariances of the parameter estimates is substantial and that taking them into account can improve matters.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008724519121
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