DEL19 Malthus

#

# DEL19 Malthus Model

#

# Measurement Matrix 

#          QA      N

#[1,] 0.7071  0.7071

#[2,] 0.7071 -0.7071

#

# Fraction of Variance 

#[1] 0.8734 1.0000

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

f <- matrix( c(   0.93889698, 0.2645764,  0.13350516,

                  0.05036955, 0.7438976, -0.03295577,

                       0.000000000,  0.0000000 ,1.0000000

),nrow=3,ncol=3, byrow=TRUE)

h <- eye(2,3)

k <- (f[,1:2,drop=FALSE])

DEL19_Malthus <- SS(F=f,H=h,K=k,z0=c( 0.13350516, -0.03295577, 1.0000000),

                  output.names=c("Growth","QA_N"))

print(DEL19_Malthus)

is.SS(DEL19_Malthus)

stability(DEL19_Malthus)

# help(simulate)

DEL19_Malthus.data <- simulate(DEL19_Malthus,sampleT=50,noise=matrix(0,50,2),start=1872)

seriesNames(outputData(DEL19_Malthus.data)) <- c("Growth","QA-N")

#tfplot(DEL19_Malthus.data)

DEL19_Malthus.datax <- simulate(DEL19_Malthus,sampleT=50,start=1872)

seriesNames(outputData(DEL19_Malthus.datax)) <- c("Growth","QA-N")

m<- l(DEL19_Malthus,TSdata(output=outputData(DEL19_Malthus.datax)))

#tfplot(m)

shockDecomposition(toSSChol(m))

tfplot(forecast(m,horizon=50))