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deriv_2nd_ma1

Analytic second derivative for MA(1) process


Description

To ease a later calculation, we place the result into a matrix structure.

Usage

deriv_2nd_ma1(theta, sigma2, tau)

Arguments

theta

A double corresponding to the theta coefficient of an MA(1) process.

sigma2

A double corresponding to the error term of an MA(1) process.

tau

A vec containing the scales e.g. 2^tau

Value

A matrix with the first column containing the second partial derivative with respect to theta, the second column contains the partial derivative with respect to theta and sigma^2, and lastly we have the second partial derivative with respect to sigma^2.

Process Haar WV Second Derivative

Taking the second derivative with respect to theta yields:

d^2/dtheta^2 nu[j]^2 (theta, sigma2) = (2*sigma2)/tau[j]

Taking the second derivative with respect to sigma^2 yields:

d^2/dsigma2^2 nu[j]^2 (theta, sigma2) = 0

Taking the first derivative with respect to theta and sigma^2 yields:

d/dtheta * d/dsigma2 nu[j]^2 (theta, sigma2) = (-6 + 2*(1 + theta)*tau[j])/tau[j]^2

Author(s)

James Joseph Balamuta (JJB)


simts

Time Series Analysis Tools

v0.1.1
AGPL-3 | file LICENSE
Authors
Stéphane Guerrier [aut, cre, cph], James Balamuta [aut, cph], Roberto Molinari [aut, cph], Justin Lee [aut], Yuming Zhang [aut], Wenchao Yang [ctb], Nathanael Claussen [ctb], Yunxiang Zhang [ctb], Christian Gunning [cph], Romain Francois [cph], Ross Ihaka [cph], R Core Team [cph]
Initial release
2019-07-21

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