Author: Gabriel Cabrera
License: MIT + file LICENSE
Overview
forecastdom is a toolkit for (un)conditional forecast dominance testing in R. It covers the full taxonomy of forecast evaluation hypotheses (unconditional and conditional, equal and superior) plus encompassing, nested-model, predictive regression, and parameter instability tests.
Tests
Forecast Comparison
| Function | Test | Reference |
|---|---|---|
dm_test() |
Diebold-Mariano (+ HLN correction) | Diebold & Mariano (1995); Harvey, Leybourne & Newbold (1997) |
cw_test() |
Clark-West MSFE-adjusted | Clark & West (2007) |
enc_new() |
ENC-NEW Encompassing | Clark & McCracken (2001) |
mse_f_test() |
McCracken MSE-F equal-MSFE | McCracken (2007) |
gw_test() |
Giacomini-White (CEPA) | Giacomini & White (2006) |
spa_test() |
Hansen’s SPA (USPA) | Hansen (2005) |
cspa_test() |
Conditional Superior Predictive Ability | Li, Liao & Quaedvlieg (2022) |
uspa_mh_test() |
Uniform Multi-Horizon SPA | Quaedvlieg (2021) |
aspa_mh_test() |
Average Multi-Horizon SPA | Quaedvlieg (2021) |
csms() |
Confidence Set for the Most Superior | Li, Liao & Quaedvlieg (2022) |
Predictive Regression & Parameter Instability
| Function | Test | Reference |
|---|---|---|
ivx_wald() |
IVX-Wald for persistent predictors | Kostakis, Magdalinos & Stamatogiannis (2015) |
qll_hat() |
Elliott-Muller parameter instability | Elliott & Muller (2006) |
Usage
Pairwise forecast comparison
library(forecastdom)
# Diebold-Mariano test with HLN correction
e1 <- rnorm(200)
e2 <- rnorm(200, mean = 0.1)
dm_test(e1, e2)Conditional Superior Predictive Ability
# Simulate data from LLQ (2022) DGP
sim <- do_sim(J = 3, n = 500, a = 1.5, c = 0, rho_u = 0.4)
# CSPA test
result <- cspa_test(sim$Y, sim$X, level = 0.05, trim = 2)
result
# Visualization
cspa_test_plot(result)Taxonomy
The package covers the forecast evaluation taxonomy from Li, Liao, and Quaedvlieg (2022):
| Equal | Superior | |
|---|---|---|
| Unconditional | dm_test() |
spa_test() |
| Conditional | gw_test() |
cspa_test() |
Performance
The CSPA test uses Rcpp-accelerated C++ code for the computationally intensive operations (Gaussian process column-max and binary search p-value computation).
References
- Clark, T.E. and McCracken, M.W. (2001). Tests of Equal Forecast Accuracy and Encompassing for Nested Models. Journal of Econometrics, 105(1), 85-110.
- Clark, T.E. and West, K.D. (2007). Approximately Normal Tests for Equal Predictive Accuracy in Nested Models. Journal of Econometrics, 138(1), 291-311.
- Diebold, F.X. and Mariano, R.S. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3), 253-263.
- Elliott, G. and Muller, U.K. (2006). Efficient Tests for General Persistent Time Variation in Regression Coefficients. Review of Economic Studies, 73(4), 907-940.
- Giacomini, R. and White, H. (2006). Tests of Conditional Predictive Ability. Econometrica, 74(6), 1545-1578.
- Hansen, P.R. (2005). A Test for Superior Predictive Ability. Journal of Business & Economic Statistics, 23(4), 365-380.
- Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the Equality of Prediction Mean Squared Errors. International Journal of Forecasting, 13(2), 281-291.
- Kostakis, A., Magdalinos, T., and Stamatogiannis, M.P. (2015). Robust Econometric Inference for Stock Return Predictability. Review of Financial Studies, 28(5), 1506-1553.
- Li, J., Liao, Z., and Quaedvlieg, R. (2022). Conditional Superior Predictive Ability. Review of Economic Studies, 89(2), 843-875.
- McCracken, M.W. (2007). Asymptotics for Out of Sample Tests of Granger Causality. Journal of Econometrics, 140(2), 719-752.
- Quaedvlieg, R. (2021). Multi-Horizon Forecast Comparison. Journal of Business & Economic Statistics, 39(1), 40-53.