Research on the Influence of Volatility of International Energy Commodity Futures Market on CPI in China

Table 3

Nanhua futures commodity index: the goodness of fit and in-sample and out-of-sample prediction accuracy at different lag orders.

Model	Index	Lag order
		15	20	23	25	30	35
Beta-MIDAS	R^2	0.8653	0.8652	0.8651	0.8650	0.8649	0.8691
	RMSE	0.4999	0.5002	0.5003	0.5004	0.5006	0.5059
	MSFE	0.2499	0.2502	0.2503	0.2504	0.2506	0.2560
	DMSFE	0.0775	0.0775	0.0774	0.0774	0.0773	0.0779
Beta-nonzero-MIDAS	R^2	0.8644	0.8641	0.8641	0.8640	0.8641	0.8685
	RMSE	0.5020	0.5024	0.5025	0.5026	0.5019	0.5079
	MSFE	0.2520	0.2524	0.2525	0.2526	0.2519	0.2579
	DMSFE	0.0771	0.0767	0.0765	0.0763	0.0759	0.0766
Exp Almon-MIDAS	R^2	0.8657	0.8657	0.8657	0.8657	0.8657	0.8699
	RMSE	0.4986	0.4986	0.4986	0.4986	0.4986	0.5038
	MSFE	0.2486	0.2486	0.2486	0.2486	0.2486	0.2538
	DMSFE	0.0777	0.0777	0.0777	0.0777	0.0777	0.0782
U-MIDAS	R^2	0.8872	0.8941	0.8944	0.8955	0.9009	0.9086
	RMSE	0.5172	0.5581	0.5680	0.5851	0.6180	0.6347
	MSFE	0.2675	0.3115	0.3227	0.3424	0.3820	0.4029
	DMSFE	0.0878	0.0912	0.0959	0.1047	0.1184	0.1344
Stepfun-MIDAS	R^2	0.8709	0.8758	0.8761	0.8762	0.8816	0.8902
	RMSE	0.5130	0.5491	0.5395	0.5486	0.5625	0.5866
	MSFE	0.2631	0.3015	0.2911	0.3009	0.3165	0.3441
	DMSFE	0.0866	0.0948	0.0909	0.0961	0.0925	0.1073
Almon-MIDAS	R^2	0.8694	0.8683	0.8685	0.8688	0.8696	0.8723
	RMSE	0.4785	0.4736	0.4715	0.4775	0.5803	0.4907
	MSFE	0.2290	0.2243	0.2223	0.2280	0.3368	0.2408
	DMSFE	0.0835	0.0895	0.0876	0.0835	0.0919	0.0808