generative_process

a list of character to choose the process.

process

a numeric vector with AR parameters if the process is "AR1" or "AR12".

a numeric vector with MA parameters if the process is "MA12".

theta

This is a generative function. The user chooses one of the <code>process</code>: "iid", "AR1", "AR12", "MA12", "Nonmixing", "sysdyn", and it
 generates the chosen process. These processes are fully described in the paper of
 E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583.
 <a href="https://arxiv.org/abs/1906.06583">https://arxiv.org/abs/1906.06583</a>.

Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.

Emmanuel Caron

Stationary Linear Models

generative_process function

This is a generative function. The user chooses one of the <code>process</code>: "iid", "AR1", "AR12", "MA12", "Nonmixing", "sysdyn", and it
 generates the chosen process. These processes are fully described in the paper of
 E. Caron, J. Dedecker and B. Michel (2019). Linear regression with stationary errors: the R package slm. arXiv preprint arXiv:1906.06583.
 <a href='https://arxiv.org/abs/1906.06583'>https://arxiv.org/abs/1906.06583</a>.

generative_process: Some stationary processes

Description

Usage

Arguments

Value

References

Examples