Nothing
context("Testing midas_r")
set.seed(1001)
n <- 250
trend <- c(1:n)
x <- rnorm(4*n)
z <- rnorm(12*n)
fn_x <- nealmon(p = c(1,-0.5),d = 8)
fn_z <- nealmon(p = c(2,0.5,-0.1),d = 17)
y <- 2 + 0.1*trend + mls(x, 0:7, 4) %*% fn_x + mls(z, 0:16, 12) %*% fn_z + rnorm(n)
accuracy <- sqrt(.Machine$double.eps)
data_ts <- list(y = ts(as.numeric(y), frequency = 1),
x = ts(x, frequency = 4),
z = ts(z, frequency = 12),
trend = trend)
test_that("midas_r with start=NULL is the same as lm",{
eq_u1<-lm(y~trend+mls(x,k=0:7,m=4)+mls(z,k=0:16,m=12))
eq_u2<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
eq_u3<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12))
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
expect_lt(sum(abs(coef(eq_u3) - coef(eq_u2))), accuracy)
})
test_that("midas_r with mlsd is the same as mls for start=NULL",{
eq_u1 <- midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12), data= data_ts)
eq_u2 <- midas_r(y~trend+mlsd(x, 0:7, y) + mlsd(z, 0:16, y), start = NULL, data = data_ts)
eq_u3 <- midas_u(y~trend+mlsd(x, 0:7, y) + mlsd(z,0:16, y), data = data_ts)
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
expect_lt(sum(abs(coef(eq_u3) - coef(eq_u2))), accuracy)
})
test_that("midas_r without weights gives the same summary as midas_u",{
a <-summary(lm(y~trend+mls(x,k=0:7,m=4)+mls(z,k=0:16,m=12)))
b <-summary(midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL), vcov = NULL)
expect_lt(sum(abs(coef(a) - coef(b))), accuracy)
})
test_that("midas_r without start throws an error",{
expect_error(midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12)))
})
test_that("midas_u picks up data from main R environment",{
eq_u1<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12))
eq_u2<-midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12),
data = list(y = y, trend = trend, x = x, z = z))
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
})
test_that("midas_r picks up data from main R environment",{
eq_u1<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
eq_u2<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),
data = list(y = y, trend = trend, x = x, z = z), start = NULL)
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
})
test_that("midas_r and midas_u picks data from the parent environment with mlsd", {
eq_u1 <- midas_u(y~trend+mls(x,0:7,4)+mls(z,0:16,12), data= data_ts)
xx <- data_ts$x
yy <- data_ts$y
zz <- data_ts$z
eq_u2 <- midas_r(yy~trend+mlsd(xx, 0:7, yy) + mlsd(zz, 0:16, yy), start = NULL)
eq_u3 <- midas_u(yy~trend+mlsd(xx, 0:7, yy) + mlsd(zz,0:16, yy))
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
expect_lt(sum(abs(coef(eq_u3) - coef(eq_u2))), accuracy)
})
test_that("NLS problem solution is close to the DGP", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
expect_lt(sum(abs(coef(a) - c(2,0.1,c(1,-0.5),c(2,0.5,-0.1)))), 1)
expect_lt(sum(abs(coef(a,midas=TRUE)-c(2,0.1,fn_x,fn_z))), 1)
})
test_that("midas_r gives the same result for mlsd", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
b <- midas_r(y~trend+mlsd(x,0:7,y,nealmon)+mlsd(z,0:16,y,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), data = data_ts)
expect_lt(sum(abs(coef(a) - coef(b))), 1e-08)
expect_lt(sum(abs(coef(a,midas=TRUE)-coef(b, midas = TRUE))), accuracy)
})
test_that("Deriv tests give positive results", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
dt<-deriv_tests(a)
expect_false(dt$first)
expect_true(dt$second)
expect_lt(sum(abs(dt$gradient))/nrow(a$model), (0.002))
})
test_that("Updating Ofunction works", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
b <- update(a, Ofunction = "nls")
c <- update(b, Ofunction = "optimx", method = c("Nelder-Mead", "BFGS", "spg"))
expect_true(a$argmap_opt$Ofunction == "optim")
expect_true(b$argmap_opt$Ofunction == "nls")
expect_true(c$argmap_opt$Ofunction == "optimx")
expect_true(inherits(b$opt, "nls"))
expect_true(inherits(c$opt, "optimx"))
expect_true(a$convergence == 0)
expect_true(b$convergence == 0)
expect_true(c$convergence == 0)
})
test_that("Updating Ofunction works for mlsd", {
a <- midas_r(y~trend+mlsd(x,0:7,y,nealmon)+mlsd(z,0:16,y,nealmon),
start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), data = data_ts)
b <- update(a, Ofunction = "nls")
c <- update(b, Ofunction = "optimx", method = c("Nelder-Mead", "BFGS", "spg"))
expect_true(a$argmap_opt$Ofunction == "optim")
expect_true(b$argmap_opt$Ofunction == "nls")
expect_true(c$argmap_opt$Ofunction == "optimx")
expect_true(inherits(b$opt, "nls"))
expect_true(inherits(c$opt, "optimx"))
expect_true(a$convergence == 0)
expect_true(b$convergence == 0)
expect_true(c$convergence == 0)
})
test_that("Updating Ofunction arguments works", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
b <- update(a, method = "CG")
expect_true(b$argmap_opt$method == "CG")
})
test_that("update works with start=NULL",{
eq_u1<-midas_r(y~trend+mls(x,0:7,4)+mls(z,0:16,12),start=NULL)
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
eq_u2 <- update(a, start = NULL)
expect_lt(sum(abs(coef(eq_u1) - coef(eq_u2))), accuracy)
})
test_that("updating gradient works",{
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
b <- update(a, weight_gradients = list(nealmon = nealmon_gradient))
expect_lt(sum(abs(b$term_info$x$gradient(c(1,0.1,0.1)) - nealmon_gradient(c(1,0.1,0.1),8))), accuracy)
})
test_that("updating data and starting values works",{
dt <- list(y = y, x = x, z = z, trend = trend)
spd <- split_data(dt, insample = 1:200, outsample = 201:250)
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),
start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)),
data = dt
)
c <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),
start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)),
data = spd$indata
)
b <- update(a, data = spd$indata, start = list(x=c(1,-0.5),z=c(2,0.5,-0.1),
`(Intercept)`= c$start_opt["(Intercept)"],
trend = c$start_opt["trend"]))
expect_lt(sum(abs(coef(b) - coef(c))), accuracy)
})
test_that("Gradient passing works", {
eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) +
mls(z, 0:16, 12, nealmon),
start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
weight_gradients=list(nealmon = nealmon_gradient))
dt <- deriv_tests(eq_r2)
expect_lt(sum(abs(dt$gradient))/nrow(eq_r2$model), 1e-3)
})
test_that("Gradient passing works for default gradients", {
eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) +
mls(z, 0:16, 12, nealmon),
start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
weight_gradients=list())
expect_lt(sum(abs(eq_r2$term_info$x$gradient(c(1,0.1,0.1)) - nealmon_gradient(c(1,0.1,0.1),8))), accuracy)
})
test_that("Gradient passing works with mlsd", {
eq_r2 <- midas_r(y ~ trend + mlsd(x, 0:7, y, nealmon) +
mlsd(z, 0:16, y, nealmon),
start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
data = data_ts,
weight_gradients = list(nealmon = nealmon_gradient))
dt <- deriv_tests(eq_r2)
expect_lt(sum(abs(dt$gradient))/nrow(eq_r2$model), 1e-3)
})
test_that("Gradient passing works for default gradients with mlsd", {
eq_r2 <- midas_r(y ~ trend + mlsd(x, 0:7, y, nealmon) +
mlsd(z, 0:16, y, nealmon),
start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
data = data_ts,
weight_gradients=list())
expect_lt(sum(abs(eq_r2$term_info$x$gradient(c(1,0.1,0.1)) - nealmon_gradient(c(1,0.1,0.1),8))), accuracy)
})
test_that("Gradient passing works for nls", {
eq_r2 <- midas_r(y ~ trend + mls(x, 0:7, 4, nealmon) +
mls(z, 0:16, 12, nealmon),
start = list(x = c(1, -0.5), z = c(2, 0.5, -0.1)),
weight_gradients=list(nealmon = nealmon_gradient), Ofunction = "nls")
dt <- deriv_tests(eq_r2)
expect_lt(sum(abs(dt$gradient))/nrow(eq_r2$model), 1e-3)
})
test_that("Term info gathering works", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
expect_named(coef(a), c("(Intercept)", "trend", "x1", "x2", "z1", "z2", "z3"))
expect_true(length(coef(a)) == 7)
expect_true(length(coef(a, midas = TRUE)) == 27)
expect_named(a$term_info, c("(Intercept)", "trend", "x", "z"))
lgs <- lapply(a$term_info,"[[","lag_structure")
expect_true(lgs[["(Intercept)"]] == 0)
expect_true(lgs[["trend"]] == 0)
expect_identical(lgs[["x"]], 0:7)
expect_identical(lgs[["z"]], 0:16)
expect_identical(a$term_info[["x"]]$weight(coef(a, term_names = "x")),
nealmon(coef(a, term_names = "x"), 8))
expect_identical(a$term_info[["z"]]$weight(coef(a, term_names = "z")),
nealmon(coef(a, term_names = "z"), 17))
})
test_that("AR* model works", {
a <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon)
+ mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
cfx <- coef(a, term_names = "x")
cfb <- a$term_info$x$weight(cfx)
mcfx <- coef(a, midas = TRUE, term_names = "x")
cfy <- coef(a, midas = TRUE, term_names = "y")
expect_lt(sum(abs(mcfx[1:4] - cfb[1:4])), accuracy)
expect_lt(sum(abs(c(cfb[5:8],rep(0,4))-cfb*cfy[1]-mcfx[5:12])), accuracy)
expect_lt(sum(abs(-cfb*cfy[2]-mcfx[13:20])), accuracy)
})
test_that("AR* model works with gradient", {
a <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon)
+ mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), weight_gradients = list())
b <- midas_r(y ~ trend + mls(y, c(1,4), 1, "*") + mls(x, 0:7, 4, nealmon)
+ mls(z, 0:16, 12, nealmon), start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)))
expect_lt(sum(abs(b$gradD(coef(a)) - a$gradD(coef(a)))), accuracy)
})
test_that("Midas_r_plain works", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), Ofunction = "optimx")
fn_a <- function(p, d) {
c(nealmon(p[1:2],d=8),nealmon(p[3:5], d=17))
}
s <- midas_r_plain(y,cbind(mls(x, 0:7, 4), mls(z, 0:16, 12)), cbind(1, trend), fn_a,
startx = a$start_opt[3:7], startz = a$start_opt[1:2])
expect_lt(sum(abs(coef(s)[c(6:7,1:5)]-coef(a)))
, accuracy)
})
test_that("Midas_r_plain gradient works", {
a <- midas_r(y~trend+mls(x,0:7,4,nealmon)+mls(z,0:16,12,nealmon),start=list(x=c(1,-0.5),z=c(2,0.5,-0.1)), Ofunction = "optimx", weight_gradients = list())
fn_a <- function(p, d) {
c(nealmon(p[1:2],d=8),nealmon(p[3:5], d=17))
}
gr_fn_a <- function(p, d) {
gr1 <- nealmon_gradient(p[1:2], d=8)
gr2 <- nealmon_gradient(p[3:5], d=17)
cbind(rbind(gr1, matrix(0, nrow = 17, ncol = 2)),
rbind(matrix(0, nrow = 8, ncol = 3), gr2))
}
s <- midas_r_plain(y,cbind(mls(x, 0:7, 4), mls(z, 0:16, 12)), cbind(1, trend), fn_a,
startx = a$start_opt[3:7], startz = a$start_opt[1:2],
grw = gr_fn_a)
expect_lt(sum(abs(coef(s)[c(6:7,1:5)]-coef(a)))
, (1e-11))
expect_lt(sum(abs(s$gradient(coef(s))[c(6:7,1:5)]-a$gradient(coef(s)[c(6:7,1:5)]))),
(1e-10))
expect_lt(sum(abs(s$gradD(coef(s))[c(26:27,1:25),c(6:7,1:5)]-a$gradD(coef(s)[c(6:7,1:5)]))),
accuracy)
})
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