cls: Conditional least square estimates for a SINAR(1,1) process.

In gilberto-sassi/sinar: Conditional Least Squared (CLS) Method for the Model SINAR(1,1)

Description

cls computes the conditional least square for a process described by

X_{i,j}= a_{10} X_{i-1,j} + a_{01} X_{i,j-1} + a_{11} X_{i-1, j-1} + ε_{i,j}

where ε_{i,j} is an iid process with poison distribution. Note the a_{10}, a_{01}, a_{11} must belong to the interval [0,1]. We obtain estimates for a_{10}, a_{01}, a_{11} and μ_ε. We do not make any asumption about the distribution of the innovation in the process.

Usage

cls(X)

Arguments

X	A integer matrix where each cell is the observed value in the regular lattice.

Value

a vector with the estimates of a_{10}, a_{01}, a_{11}, μ.

Examples

data("nematodes")
cls(nematodes)

gilberto-sassi/sinar documentation built on Nov. 16, 2020, 1:42 p.m.