num.jacobian: Compute the symmetric numerical first order derivatives of a...

Description Usage Arguments Value Note Author(s) References Examples

Description

Compute the symmetric numerical first order derivatives of a multivariate function.

Usage

1
num.jacobian(fct_handle, x, prec)

Arguments

fct_handle

Name of a function returning a N x 1 vector.

x

Point (d x 1) of evaluation at which the derivatives will be computed.

prec

Percentage of +\- around x (in fraction).

Value

J

Derivatives (N x d)

Note

Translated from Matlab by David-Shaun Guay (HEC Montreal grant).

Author(s)

Bruno Remillard

References

Appendix B of 'Statistical Methods for Financial Engineering, B. Remillard, CRC Press, (2013).

Examples

1
2
data(data.cir)
out = est.cir(data.cir,method='num')

Example output

Loading required package: ggplot2
Loading required package: reshape
Loading required package: corpcor
Are you a satisfied with the graph? 


The estimated coefficients correspond to the annualized spot rate (##)
 Fisher information computed with the numerical gradient (Appendix B.5.1)


 alpha = 0.5092 /+ 1.0909 

  beta = 2.4562 /+ 1.3785 

 sigma = 0.3486 /+ 0.0203 

    q1 = 0.3244 /+ 9.3209 

    q2 = -0.2471 /+ 3.3111 

   phi = 0.9986,  phiest = 0.9986 /+ 0.0030 

SMFI5 documentation built on May 2, 2019, 10:25 a.m.

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