The Duality of a Dynamic Model of Equilibrium and an Optimal Growth Model: The Heterogeneous Capital Goods Case

1981 ◽  
Vol 96 (2) ◽  
pp. 271 ◽  
Author(s):  
Robert A. Becker
Keyword(s):  
Dynamic Model ◽  
Growth Model ◽  
Optimal Growth ◽  
Capital Goods

An Optimal Growth Model with Time Lags

Econometrica ◽  
1972 ◽  
Vol 40 (6) ◽  
pp. 1137 ◽  
Author(s):  
Mohamed A. El-Hodiri ◽  
Edna Loehman ◽  
Andrew Whinston
Keyword(s):  
Growth Model ◽  
Optimal Growth ◽  
Time Lags

An Optimal Greenhouse Tax in an Optimal Growth Model

1996 ◽  
Vol 63 (2) ◽  
pp. 418 ◽  
Author(s):  
Kenneth A. Lewis ◽  
Laurence S. Seidman
Keyword(s):  
Growth Model ◽  
Optimal Growth

System Transition to the “Eco-Finance” Sustainable Growth: Evidence from Chinese and Russian Oil and Gas Companies

2019 ◽  
Vol 2019 (4) ◽  
pp. 42-59
Author(s):  
Alina Стеблянская ◽  
Vang Zhen ◽  
Nailya Gabdrahmanova ◽  
Uvais Aleroev
Keyword(s):  
Dynamic Model ◽  
Ricci Curvature ◽  
Growth Model ◽  
Oil And Gas ◽  
Dynamic Modelling ◽  
Growth Index ◽  
Oil And Gas Industry ◽  
Sustainable Growth ◽  
System Transition ◽  
Oil And Gas Companies

In this paper, we study how financial sustainable growth matters for environment protection and social responsibility. Financial decisions that take into account long-term social and environmental consequences should be central to the system of financially sustainable growth of oil and gas companies of the world, including Russia and China. Authors present Russia and China oil and gas industry system transition to «Green Finance» sustainable growth model results with interrelation financial, environmental and social factors. Study regarded in the context of economic growth theory, system economics theory and the theory of relativity. It was created Python 3.6. financial sustainable growth model for Russia and China. A dynamic model was built on the basis of the Ricci tensor. For sustainable growth intensity analysis, it was compared Financial sustainable index (FSI), Higgins’ sustainable growth rate (SGR), Ivashkovskaya’ sustainable growth index (SGI), Ivashkovskaya’ sustainable growth index modification (SGImodif) every period indicators’ average values and the graph’ average values of the Ricci curvatures (R). Using the Ricci curvature, the parameters of a dynamic model of sustainable growth are determined for Russia and for China oil and gas industry.Key words: sustainable growth, System Dynamic modelling, Ecological Finance, Ricci Curvature, environmental financial sustainable growth, Financial Sustainable Index (FSI).

scholarly journals Consumption paths under prospect utility in an optimal growth model

2011 ◽  
Vol 35 (3) ◽  
pp. 273-281 ◽  
Author(s):  
Reto Foellmi ◽  
Rina Rosenblatt-Wisch ◽  
Klaus Reiner Schenk-Hoppé
Keyword(s):  
Growth Model ◽  
Optimal Growth

scholarly journals Estimation of the final size of the COVID-19 epidemic

2020 ◽  
Author(s):  
Milan Batista
Keyword(s):  
Dynamic Model ◽  
Growth Model ◽  
Short Paper ◽  
Logistic Growth ◽  
Final Size ◽  
Logistic Growth Model

AbstractIn this short paper, the logistic growth model and classic susceptible-infected-recovered dynamic model are used to estimate the final size of the coronavirus epidemic.

scholarly journals Optimal exponent-pairs for the Bertalanffy-Pütter growth model

2018 ◽  
Author(s):  
Katharina Renner-Martin ◽  
Norbert Brunner ◽  
Manfred Kühleitner ◽  
Werner-Georg Nowak ◽  
Klaus Scheicher
Keyword(s):  
Differential Equation ◽  
Growth Model ◽  
Explanatory Power ◽  
Information Criterion ◽  
Optimal Growth ◽  
Growth Function ◽  
Data Fitting ◽  
Model Parameters ◽  
Data Set ◽  
Von Bertalanffy Growth Function

The Bertalanffy-Pütter growth model describes mass m at age t by means of the differential equation dm/dt = p⋅ma−q⋅mb. The special case using the Bertalanffy exponent-pair a=2/3 and b=1 is most common (it corresponds to the von Bertalanffy growth function VBGF for length in fishery literature). For data fitting using general exponents, five model parameters need to be optimized, the pair a<b of non-negative exponents, the non-negative constants p and q, and a positive initial value m0 for the differential equation. For the case b=1 it is known that for most fish data any exponent a<1 could be used to model growth without affecting the fit to the data significantly (when the other parameters p, q, m0 were optimized). Thereby, data fitting used the method of least squares, minimizing the sum of squared errors (SSE). It was conjectured that the optimization of both exponents would result in a significantly better fit of the optimal growth function to the data and thereby reduce SSE. This conjecture was tested for a data set for the mass-growth of Walleye (Sander vitreus), a fish from Lake Erie, USA. Compared to the Bertalanffy exponent-pair the optimal exponent-pair achieved a reduction of SSE by 10%. However, when the optimization of additional parameters was penalized, using the Akaike information criterion (AIC), then the optimal exponent-pair model had a higher (worse) AIC, when compared to the Bertalanffy exponent-pair. Thereby SSE and AIC are different ways to compare models. SSE is used, when predictive power is needed alone, and AIC is used, when simplicity of the model and explanatory power are needed.

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