###
### Example 6: comparison with the tempdisagg package for R
### on the "swisspharma" dataset
###

set verbose off
clear

# run tempdisagg to prepare the data and provide a comparison
foreign language=R --quiet
    library(tempdisagg)
    data(swisspharma)

    exports <- ts(exports.q, start=c(1975,1), end=c(2010,4), frequency=4)

    m0 <- td(sales.a ~ 0, to=4, method="denton-cholette")
    m1 <- td(sales.a ~ 0 + exports, to=4, method="denton-cholette")
    m2 <- td(sales.a ~ 1 + exports, to=4, method="chow-lin-maxlog")
    m3 <- td(sales.a ~ 1 + exports, to=4, method="chow-lin-minrss-quilis")

    x0 <- predict(m0)
    x1 <- predict(m1)
    x2 <- predict(m2)
    x3 <- predict(m3)

    x <- matrix(c(exports,x0,x1,x2,x3), nrow = 144)
    gretl.export(x)
    
    salesa <- as.matrix(sales.a)
    gretl.export(salesa)
end foreign

# set up a gretl dataset and import the data from R
    
nulldata 144
setobs 4 1975:1 
X = mread("x.mat", 1)
salesa = mread("salesa.mat", 1)

series exports = X[,1]

bundle OPTIONS = _(aggtype="sum")

OPTIONS.method = "denton-pfd"
series z0 = tdisagg(salesa, , 4, OPTIONS)
series x0 = X[,2]

series z1 = tdisagg(salesa, exports, 4, OPTIONS)
series x1 = X[,3]

OPTIONS.method = "chow-lin-mle"
series z2 = tdisagg(salesa, exports, 4, OPTIONS)
series x2 = X[,4]

OPTIONS.method = "chow-lin-ssr"
series z3 = tdisagg(salesa, exports, 4, OPTIONS)
series x3 = X[,5]

# check that the results are (numerically) the same

ols z0 const x0 --simple
ols z1 const x1 --simple
ols z2 const x2 --simple
ols z3 const x3 --simple