## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4
)

## ----setup--------------------------------------------------------------------
library(forecastdom)
set.seed(42)

## ----dm-----------------------------------------------------------------------
e1 <- rnorm(200)
e2 <- rnorm(200, mean = 0.15)

dm_test(e1, e2)

## ----cw-----------------------------------------------------------------------
actual <- rnorm(200)
f1 <- actual + rnorm(200, sd = 0.5) # benchmark (restricted)
f2 <- actual + rnorm(200, sd = 0.4) # alternative (unrestricted)

cw_test(actual - f1, actual - f2, f1, f2)

## ----gw-----------------------------------------------------------------------
gw_test(e1, e2)

## ----spa----------------------------------------------------------------------
sim <- do_sim(J = 3, n = 250, a = 1, c = 0, rho_u = 0.4)
spa_test(sim$Y, level = 0.05, B = 1000)

## ----cspa-null----------------------------------------------------------------
# Under the null (a = 1) the benchmark is weakly dominant
sim_null <- do_sim(J = 3, n = 500, a = 1, c = 0, rho_u = 0.4)
cspa_test(sim_null$Y, sim_null$X, level = 0.05, trim = 2)

## ----cspa-alt-----------------------------------------------------------------
# Under the alternative (a = 1.5) one competitor beats the benchmark in some states
sim_alt <- do_sim(J = 3, n = 500, a = 1.5, c = 0, rho_u = 0.4)
result <- cspa_test(sim_alt$Y, sim_alt$X, level = 0.05, trim = 2)

result

## ----cspa-plot----------------------------------------------------------------
cspa_test_plot(result)

## ----csms, eval = FALSE-------------------------------------------------------
# csms(losses, X, level = 0.05, trim = 2, method_names = c("AR1", "HAR", "HARQ", "LASSO"))

## ----ivx----------------------------------------------------------------------
n <- 300
x <- cumsum(rnorm(n))
y <- 0.02 * x + rnorm(n)

ivx_wald(y, as.matrix(x), K = 1, M_n = floor(n^(1/3)))

## ----qll----------------------------------------------------------------------
X <- matrix(rnorm(n * 2), n, 2)
y2 <- X %*% c(0.5, -0.3) + rnorm(n)

qll_hat(y2, X, L = floor(n ^ (1 / 3)))