Estimation of Selected Production Functions Using Starting Parameters Given by Stochastic Funnel Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F20%3A43918274" target="_blank" >RIV/62156489:43110/20:43918274 - isvavai.cz</a>
Result on the web
<a href="https://mme2020.mendelu.cz/wcd/w-rek-mme/mme2020_conference_proceedings_final.pdf" target="_blank" >https://mme2020.mendelu.cz/wcd/w-rek-mme/mme2020_conference_proceedings_final.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Estimation of Selected Production Functions Using Starting Parameters Given by Stochastic Funnel Algorithm
Original language description
In this paper we deal with the problem of finding starting parameters for numerical methods designed for estimation of nonlinear production functions. Appropriateness of these starting parameters has a major impact on success of inding stable final parameters estimation. Bad starting parameters can cause the estimate of production function unacceptable from an economic point of view, or divergence of the estimation iteration process. We are focused on two approaches of searching starting parameters: the first one is based on establishing 'self-starting' models and searching preliminary estimates systematically in grid or by random shooting. As the second approach we use so-called stochastic funnel algorithm. We verify deployment of this algorithm for production functions and extend its usage with the possibility of searching for negative parameters. To assess mentioned methods we provide simulation study based on selected production functions. Our results show that stochastic funnel algorithm is able to give starting parameters comparable to the tradition methods for the CES type production functions, and visibly better for the Sato production function.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Mathematical Methods in Economics 2020: Conference Proceedings
ISBN
978-80-7509-734-7
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
630-635
Publisher name
Mendelova univerzita v Brně
Place of publication
Brno
Event location
Brno
Event date
Sep 9, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000668460800096