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Projects / Programmes source: ARIS

Analysis of numerical methods for solving dznamic economic models

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Research activity

Code Science Field Subfield
5.02.00  Social sciences  Economics   

Code Science Field
S180  Social sciences  Economics, econometrics, economic theory, economic systems, economic policy 
Keywords
numerical methods, markovian dynamic models, computer programming, derivation of numerical algorithms.
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publications
1.  16312  PhD Igor Bernik  Administrative and organisational sciences  Head  2006 - 2007  0 
2.  27774  Mitja Čepič Vogrinčič  Educational studies  Researcher  2006 - 2007  0 
3.  26390  Tomaž Dintinjana  Political science  Researcher  2005  0 
4.  14558  PhD Mićo Mrkaić  Economics  Researcher  2005  0 
5.  24556  PhD Rado Pezdir  Economics  Researcher  2005 - 2007  0 
Organisations (2)
no. Code Research organisation City Registration number No. of publications
1.  0586  University of Maribor, Faculty of Organizational Sciences  Kranj  5089638018  0 
2.  1636  Institute of Civilisation and culture, ICC  Ljubljana  1196901  0 
Abstract
The working hypothesis is that extant numerical algorithms for solving dynamic economic models can be significantly accelerated. These algorithms use expansion of value functions and expressing the value functions and/or policy functions in terms of neural networks can accelerate policy functions in terms of orthogonal or complete polynomials. The core of the problem with computing numerical solutions to dynamic economic models is the so-called “curse of dimensionality”, which exhibits itself in the rapid growth of time and computer memory, which are needed to solve dynamic economic models. This growth is exponential in the number of state variables, which describe the state of a given economic system. It follows that faster algorithms should be developed, which will expand the space of those dynamic economic models, which can be analysed and studied by researchers.
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