estimation_LRM function

Estimation of the t-Levy Regression Model

The function estimates a t-Levy Regression Model

estimation_LRM(start, model, data, upper, lower, PT = 500, n_obs1 = NULL)

Arguments

• start: Initial values to be passed to the optimizer.
• model: A yuima.LevyRM-class that contains the mathematical representation of the t-Levy Regression Model. Its slot @data can contain either real or simulated data.
• data: An object of class yuima.data-class contains the observations available at uniformly spaced time. If data=NULL, the default, the function uses the data in the object model.
• upper: A named list for specifying upper bounds of parameters.
• lower: A named list for specifying lower bounds of parameters.
• PT: The number of the data for the estimation of the regressor coefficients and the scale parameter.
• n_obs1: The number of data used in the estimation of the degree of freedom. As default the number of the whole data is used in this part

Details

A two-step estimation procedure. Regressor coefficients and scale parameters are obtained by maximizing the quasi-likelihood function based on the Cauchy density. The degree of freedom is estimated used the unitary increment of the t-noise.

Returns

Estimated parameters

Author(s)

The YUIMA Project Team

Contacts: Lorenzo Mercuri lorenzo.mercuri@unimi.it