Endogenous Growth, Capital Utilization and Depreciation

We study the one sector model of growth when a linear production technology is combined with adjustment costs and a technology for capital maintenance. Agents are allowed to under-use the installed capital and to vary the depreciation rate. This economy decides endogenously how much resources devotes to the accumulation of new capital and how much to maintenance and repair activities. We find as striking results that the long-run depreciation and capital utilization rates are positively related to the population growth rate, and that both depend negatively on the initial conditions. The long-run growth rate appears positively correlated with the depreciation rate.Maintenance; Depreciation; Capital Utilization; Endogenous Growth

Renewable Natural Resources and Endogenous Growth

We study a two-sector endogenous growth model where a single consumption good is obtained using a renewable resource in combination with physical capital. Both inputs are essential for production and technical substitutes. In this context we analyze the issues of sustainability, long-run and short-run growth as well as convergence, associated with the competitive equilibrium solution trajectories. We show that efficiency, long-run growth and sustainability are both compatible in a natural resource based production economy.Natural Capital, Endogenous Growth, Sustainability, Convergence

Closed-Form Solution for a Two-Sector Endogenous Growth Model with two Controls

In this paper we show a method for solving in closed form a particular family of four-dimension non-linear modified Hamiltonian dynamic systems, with two states and two co-states and two co-states, which arises from a two-sector endogenous growth model where the physical capital stock is combined with a renewable natural capital stock as essential inputs for productionNon-Linear Dynamic System, Analytical Solution, Endogenous Growth, Transitional Dynamics

On the use of machine learning algorithms in the measurement of stellar magnetic fields

Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles. Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of the inversions diminish considerably when noise is taken into account. In consequence, we propose a data pre-process in order to reduce the noise impact, which consists in a denoising profile process combined with an iterative inversion methodology. Applying this data pre-process, we have found a considerable improvement of the inversions results, allowing to estimate the errors associated to the measurements of stellar magnetic fields at different noise levels. We have successfully applied our data analysis technique to two different stars, attaining by first time the measurement of H_eff from multi-line profiles beyond the condition of line autosimilarity assumed by other techniques.Comment: Accepted for publication in A&