do_sim: Simulate Data from the CSPA Paper DGP

In forecastdom: (Un)Conditional Forecast Dominance

Simulate Data from the CSPA Paper DGP

Description

Generates data from the data generating process in Section 3.1 of Li, Liao, and Quaedvlieg (2022). The conditioning variable X_t follows a Gaussian AR(1) with coefficient 0.5, and the loss differentials are Y_{j,t} = 1 - a \exp(-(X_t - c)^2) + u_{j,t}, where u_{j,t} follows an AR(1) with coefficient rho_u.

Usage

do_sim(J, n, a, c, rho_u)

Arguments

J	Integer; number of competing forecast methods.
n	Integer; sample size.
a	Numeric; controls the shape of the conditional mean function. a = 1 gives the null hypothesis (h_j(c) = 0).
c	Numeric; location parameter for the minimum of h_j(x).
rho_u	Numeric in [0, 1); AR(1) coefficient of the error process. Higher values induce more serial dependence.

Value

A list with components:

Y	An n x J matrix of loss differentials.
X	A numeric vector of length n.

Examples

sim <- do_sim(J = 3, n = 250, a = 1, c = 0, rho_u = 0.4)
str(sim)

forecastdom documentation built on Nov. 2, 2026, 1:10 a.m.