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Selected Publication:

Brunner, N; Mayrpeter, G; Kuhleitner, M.
(2022): Parameter estimation of the Solow-Swan fundamental differential equation
HELIYON. 2022; 8(10), e10816 FullText FullText_BOKU

Abstract:
Background: The Solow-Swan model describes the long-term growth of the capital to labor ratio by the funda-mental differential equation of Solow-Swan theory. In conventional approaches, this equation was fitted to data using additional information, such as the rates of population growth, capital depreciation, or saving. However, this was not the best possible fit.Objectives: Using the method of least squares, what is the best possible fit of the fundamental equation to the time -series of the capital to labor ratios? Are the best-fit parameters economically sound?Method: For the data, we used the Penn-World Table in its 2021 version and compared six countries and three definitions of the capital to labor ratio. For optimization, we used a custom-made variant of the method of simulated annealing. We also compared different optimization methods and calibrations.Results: When comparing different methods of optimization, our custom-made tool provided reliable parameter estimates. In terms of R-squared they improved upon the parameter estimates of the conventional approach. Except for the USA, the best-fit values of the exponent were unplausible, as they suggested a too large elasticity of output. However, using a different calibration resulted in more plausible values of the best-fit exponent also for France and Pakistan, but not for Argentina and Japan.Conclusion: Our results have shown a discrepancy between the best-fit parameters obtained from optimization and the parameter values that are deemed plausible in economy. We propose a research program to resolve this issue by investigating if suitable calibrations may generate economically plausible best-fit parameter values.
Authors BOKU Wien:
Brunner Norbert
Kühleitner Manfred
BOKU Gendermonitor:


Find related publications in this database (Keywords)
Akaike information criterion(AIC)
R-squared(R2)
Simulated annealing


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