Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments

Table 1

Forecast comparison under multicollinearity from Monte Carlo simulation (Scenario 1).

Models	ρ = 0.25,  = 50		ρ = 0.25,  = 70
n = 100/200/400	RMSE	MAE	RMSE	MAE
MCP	1.123/1.055/1.027	0.908/0.848/0.821	1.205/1.069/1.031	0.971/0.858/0.825
E-SCAD	1.135/1.066/1.034	0.917/0.856/0.827	1.195/1.086/1.040	0.961/0.872/0.831
Autometrics	1.316/1.091/1.027	1.065/0.874/0.822	1.316/1.091/1.042	1.065/0.874/0.834
FM_PCA	3.517/3.210/2.829	2.839/2.576/2.260	4.493/4.305/3.966	3.623/3.458/3.173
FM_PLS	1.528/1.200/1.090	1.235/0.963/0.871	1.921/1.321/1.126	1.551/1.059/0.901
n = 100/200/400	ρ = 0.5,  = 50		ρ = 0.5,  = 70
MCP	1.145/1.056/1.027	0.925/0.848/0.821	1.318/1.069/1.032	1.062/0.858/0.825
E-SCAD	1.112/1.058/1.030	0.898/0.849/0.824	1.168/1.074/1.035	0.940/0.862/0.827
Autometrics	1.156/1.062/1.027	0.931/0.853/0.821	1.473/1.091/1.041	1.191/0.874/0.833
FM_PCA	2.583/2.053/1.705	2.088/1.644/1.365	3.933/3.334/2.700	3.174/2.677/2.164
FM_PLS	1.368/1.161/1.080	1.105/0.932/0.864	1.595/1.248/1.108	1.287/1.001/0.886
n = 100/200/400	ρ = 0.9,  = 50		ρ = 0.9,  = 70
MCP	1.484/1.157/1.042	1.198/0.930/0.832	1.764/1.261/1.058	1.424/1.013/0.846
E-SCAD	1.201/1.060/1.019	0.968/0.851/0.814	1.291/1.080/1.021	1.040/0.867/0.817
Autometrics	4.363/1.795/1.031	3.528/1.443/0.825	6.589/2.501/1.053	5.333/2.006/0.843
FM_PCA	1.169/1.099/1.075	0.943/0.883/0.859	1.318/1.212/1.165	1.065/0.974/0.932
FM_PLS	1.138/1.078/1.043	0.919/0.865/0.834	1.184/1.095/1.053	0.959/0.880/0.842

Note. Bold values indicate a better forecast.