USL20 HealthCare

# Cut-and-Paste Code below into Window Above and Run

#

# USHC BAU Model

#

# Measurement Matrix 

# (Growth - Hospitals) (N + L + OH - Q) (Growth - N - Capital)

#     P.HC. GDP.HC.  N.PI.   K.HC.  L.HC   O.H.

#[1,]  0.4324   0.432  0.339  0.4317 0.427 -0.379

#[2,] -0.0762  -0.130  0.763 -0.0865 0.210  0.586

#[3,]  0.4448   0.288 -0.466 -0.2307 0.362  0.564

#

# Fraction of Variance 

#[1] 0.881 0.991 0.998 1.000 1.000 1.000

#

require(dse)

require(matlab)

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

f <- matrix( c(1.05997234, 0.002735686,  0.03889518,  0.17280925,

                    -0.03298602, 0.945133060, -0.23385045,  0.02194855,

                    -0.01436626, 0.027867381,  1.02834689, -0.01447672,

                       0.000000000, 0.00000000,  0.0000000,  1.00000000

),byrow=TRUE,nrow=4,ncol=4)

#

# To Stabilize, Uncomment Next Line

# f[1,1] <- 1; f[2,2] <- 0.898812136; f[3,3] <- 0.97794766

#

h <- eye(3,4)

k <- f[1:4,1:3,drop=FALSE]

USHC=SS(f,H=h,K=k,z0=c(0.17280925,  0.02194855, -0.01447672,  

1.0000000),

              output.names=c("HC1","HC2","HC3"))

print(USHC)

stability(SS(f[1:3,1:3,drop=FALSE],H=eye(3),R=eye(3),Q=eye(3)))

# tfplot(simulate(USHC,sampleT=50,start=1950))

shockDecomposition(toSSChol(USHC))

USHC.data <- simulate(USHC,sampleT=50,noise=matrix(0,50,3))

#

# RANDOM WALK MODEL (uncomment next line as above)

# USHC.data <- simulate(USHC,sampleT=50)

#

m <- l(USHC,USHC.data)

# tfplot(m)

AIC(m)

USHC.f <- forecast(m,horizon=50)

tfplot(USHC.f)