Estimation of Selected Production Functions Using Starting Parameters Given by Stochastic Funnel Algorithm

Kód výsledku v 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>

Výsledek na webu

<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

—

Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of Selected Production Functions Using Starting Parameters Given by Stochastic Funnel Algorithm

Popis výsledku v původním jazyce

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 &apos;self-starting&apos; 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.

Název v anglickém jazyce

Estimation of Selected Production Functions Using Starting Parameters Given by Stochastic Funnel Algorithm

Popis výsledku anglicky

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 &apos;self-starting&apos; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Mathematical Methods in Economics 2020: Conference Proceedings

ISBN

978-80-7509-734-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

630-635

Název nakladatele

Mendelova univerzita v Brně

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

9. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000668460800096