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

Table 2

Forecast comparison under heteroscedasticity from Monte Carlo simulation (Scenario 2).

Models	σ = 0.1/0.3,  = 50		σ = 0.1/0.3,  = 70
n = 100/200/400	RMSE	MAE	RMSE	MAE
MCP	0.313/0.306/0.303	0.253/0.246/0.242	0.321/0.307/0.303	0.260/0.246/0.242
E-SCAD	0.319/0.309/0.304	0.258/0.248/0.243	0.331/0.311/0.305	0.267/0.249/0.243
Autometrics	0.318/0.308/0.303	0.256/0.248/0.242	0.339/0.313/0.305	0.274/0.250/0.244
FM_PCA	3.373/3.055/2.648	2.723/2.452/2.115	4.382/4.197/3.847	3.534/3.374/3.078
FM_PLS	0.399/0.327/0.311	0.322/0.262/0.249	0.625/0.347/0.317	0.504/0.278/0.253
n = 100/200/400	σ = 0.2/0.6,  = 50		σ = 0.2/0.6,  = 70
MCP	0.627/0.613/0.606	0.507/0.492/0.484	0.643/0.614/0.607	0.520/0.492/0.485
E-SCAD	0.637/0.617/0.609	0.515/0.496/0.486	0.659/0.621/0.609	0.532/0.498/0.487
Autometrics	0.636/0.617/0.606	0.512/0.496/0.484	0.667/0.625/0.610	0.548/0.501/0.488
FM_PCA	3.410/3.101/2.704	2.753/2.489/2.160	4.412/4.233/3.883	3.556/3.402/3.106
FM_PLS	0.798/0.654/0.623	0.646/0.525/0.498	1.107/0.693/0.634	0.892/0.556/0.507
n = 100/200/400	σ = 0.3/0.9,  = 50		σ = 0.3/0.9,  = 70
MCP	0.941/0.920/0.909	0.761/0.739/0.727	0.965/0.921/0.910	0.780/0.739/0.728
E-SCAD	0.954/0.926/0.913	0.771/0.743/0.730	0.985/0.930/0.914	0.795/0.746/0.730
Autometrics	0.954/0.926/0.909	0.768/0.744/0.727	1.017/0.938/0.916	0.823/0.752/0.733
FM_PCA	3.478/3.176/2.791	2.809/2.549/2.230	4.467/4.281/3.941	3.601/3.440/3.153
FM_PLS	1.181/0.983/0.935	0.956/0.789/0.748	1.507/1.040/0.951	1.215/0.834/0.760

Note. Bold values indicate a better forecast.