score_test_nonlinpq_h0 {PNAR} | R Documentation |
Linearity test against non-linear ID-PNAR(p) model
Description
Quasi score test for testing linearity of Poisson Network Autoregressive model
of order against the non-linear Intercep Drift (ID) version
(ID-PNAR(
)).
Usage
score_test_nonlinpq_h0(b, y, W, p, d, Z = NULL)
Arguments
b |
The estimated parameters from the linear PNAR model, in the following order:
(intercept, network parameters, autoregressive parameters, covariates).
The dimension of the vector should be |
y |
A |
W |
The |
p |
The number of lags in the model. |
d |
The lag parameter of non-linear variable (should be between 1 and |
Z |
An |
Details
The function computes the quasi score test for testing linearity of Poisson Network Autoregressive model of order against the following ID-PNAR(
) model. For each node of the network
over the time sample
where is the network effect, i.e. the weighted average impact of node
connections, with the weights of the mean being
, the single element of the network matrix
. The sequence
is the expectation of
conditional to its past values.
The null hypothesis of the test is defined as , versus the alternative
. The test statistic has the form
where
is the partition of the quasi score related to the non-linear parameter , evaluated at the estimated parameters
under the null assumption
(linear model), and
is the variance of
. Under
, the test asymptotically follows the
distribution with 1 degree of freedom. For details see Armillotta and Fokianos (2022b, Sec. 4).
Value
A list with attribute class "htest" including:
statistic |
The value of the |
parameter |
The degrees of freedom of the |
p.value |
The p-value of the |
null.value |
The value of the |
alternative |
The alternative hypothesis, |
method |
The name of the test. |
data.name |
Information on the arguments used. |
Alternatively, these can be printed via the function summary.nonlin
.
Author(s)
Mirko Armillotta, Michail Tsagris and Konstantinos Fokianos.
References
Armillotta, M. and K. Fokianos (2022a). Poisson network autoregression. https://arxiv.org/abs/2104.06296
Armillotta, M. and K. Fokianos (2022b). Testing linearity for network autoregressive models. https://arxiv.org/abs/2202.03852
Armillotta, M., Tsagris, M. and Fokianos, K. (2022c). The R-package PNAR for modelling count network time series. https://arxiv.org/abs/2211.02582
See Also
score_test_stnarpq_j, score_test_tnarpq_j,
lin_estimnarpq
Examples
data(crime)
data(crime_W)
mod1 <- lin_estimnarpq(crime, crime_W, p = 2)
ca <- mod1$coefs[, 1]
score_test_nonlinpq_h0(ca, crime, crime_W, p = 2, d = 1)