Capital Asset Pricing for Nature
Defining Approximation Space
catch function of GOM dataset
Generating Unidimensional Chebyshev polynomial (monomial) basis
Generating Chebyshev grids
Unidimensional Chebyshev nodes
first derivative function of sdot in GOM dataset
second derivative function of sdot in GOM dataset
first derivative function of profit in GOM dataset
second derivative function of profit in GOM dataset
effort function of GOM dataset
Prey-Predator (Lotka-Volterra) example: two stocks
Prey-Predator (Lotka-Volterra) example in LV dataset
Prey-Predator (Lotka-Volterra) example in LV dataset
Calculating P-approximation coefficients
the parameter vector adopted in GOM dataset
Calculating Pdot-approximation coefficients
Simulation of Pdot-approximation
Plot Generator for Shadow Price or Value Function
profit function in GOM dataset
Simulation of P-approximation
growth function of GOM dataset
Generating unifrom grids
Calculating V-approximation coefficients
Simulation of V-approximation
Implements approximation methods for natural capital asset prices suggested by Fenichel and Abbott (2014) <doi:10.1086/676034> in Journal of the Associations of Environmental and Resource Economists (JAERE), Fenichel et al. (2016) <doi:10.1073/pnas.1513779113> in Proceedings of the National Academy of Sciences (PNAS), and Yun et al. (2017) in PNAS (accepted), and their extensions: creating Chebyshev polynomial nodes and grids, calculating basis of Chebyshev polynomials, approximation and their simulations for: V-approximation (single and multiple stocks, PNAS), P-approximation (single stock, PNAS), and Pdot-approximation (single stock, JAERE). Development of this package was generously supported by the Knobloch Family Foundation.