lin_narpq_init: Starting values for the linear Poisson NAR(p) model model...

In PNAR: Poisson Network Autoregressive Models

Starting values for the linear Poisson NAR(p) model model with p lags and q covariates (PNAR(p))

Description

Starting values for the linear Poisson Network Autoregressive model of order p with q covariates (PNAR(p)).

Usage

lin_narpq_init(y, W, p, Z = NULL)

Arguments

y	A TT x N time series object or a TT x N numerical matrix with the N multivariate count time series over TT time periods.
W	The N x N row-normalized non-negative adjacency matrix describing the network. The main diagonal entries of the matrix should be zeros, all the other entries should be non-negative and the maximum sum of elements over the rows should equal one. The function row-normalizes the matrix if a non-normalized adjacency matrix is provided.
p	The number of lags in the model.
Z	An N x q matrix of covariates (one for each column), where q is the number of covariates in the model. Note that they must be non-negative.

Details

The function computes starting values to be used in the function lin_estimnarpq. These are simply the ordinary least squares estimators with a correction. If any of the the resulting coefficients is negative they become equal to 0.001.

Value

A vector with the initial values.

Author(s)

Mirko Armillotta, Michail Tsagris and Konstantinos Fokianos.

References

Armillotta, M. and K. Fokianos (2023). Nonlinear network autoregression. Annals of Statistics, 51(6): 2526–2552.

Armillotta, M. and K. Fokianos (2024). Count network autoregression. Journal of Time Series Analysis, 45(4): 584–612.

Armillotta, M., Tsagris, M. and Fokianos, K. (2024). Inference for Network Count Time Series with the R Package PNAR. The R Journal, 15/4: 255–269.

See Also

lin_estimnarpq

Examples

data(crime)
data(crime_W)
x0 <- lin_narpq_init(crime, crime_W, p = 2)

PNAR documentation built on Sept. 12, 2024, 7:30 a.m.