The impact of economic policy uncertainty on the innovation in China: Empirical evidence from autoregressive distributed lag bounds tests

Figures & data

Figure 3. Impulse response function (combined graph).

Table 1. Descriptive statistics and correlation matrices

	LNN	UNC	GDP
Mean	4.953230	2.016851	9.481980
Median	4.970742	2.027740	9.399813
Maximum	6.004496	2.388098	14.23139
Minimum	4.000477	1.745757	6.914330
Std. Dev.	0.664766	0.174092	1.824493
Skewness	0.046708	0.360019	0.873710
Kurtosis	1.680454	2.249712	3.514029
Jarque-Bera	1.531187	0.946212	2.902989
Probability	0.465058	0.623064	0.234220
	LNN	UNC	GDP
LNN	1.000000
UNC	0.603756	1.000000
GDP	0.132076	0.356531	1.000000

Table 2. The results of unit root tests

Variables	ADF	DF-GLS Test
LNN	−4.34(1)**	−2.89(0)**
UNC	−4.30(0)*	−2.60(0)*
GDP	−4.11(1)**	−4.14(0)**
$\mathrm{\Delta }$LNN	−4.10(1)*	−2.90(0)**
$\mathrm{\Delta }$UNC	−3.87(0)**	−3.92(0)*
$\mathrm{\Delta }$GDP	−4.01(1)**	−4.04(1)**

Notes: The ** and * indicate significance at 1% and 5% levels, respectively. The table in the parenthesis is the test of ADF and DF-GLS (unit root test). Null hypotheses: Data are not non-stationary.

Table 3. ARDL bounds testing to co-integration analysis

Bounds testing to co-integration
Estimated model	$\mathrm{I}\mathrm{N}{\mathrm{N}}_{\mathrm{t}}=\mathrm{f}\left(\mathrm{U}\mathrm{N}{\mathrm{C}}_{\mathrm{t}},\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{t}}\right)$	$\mathrm{U}\mathrm{N}{\mathrm{C}}_{\mathrm{t}}=\mathrm{f}\left(\mathrm{I}\mathrm{N}{\mathrm{N}}_{\mathrm{t}},\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{t}}\right)$	$\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{t}}=\mathrm{f}(\mathrm{I}\mathrm{N}{\mathrm{N}}_{\mathrm{t}},\mathrm{U}\mathrm{N}{\mathrm{C}}_{\mathrm{t}}$)
Optimal lag length	(2)	(3)	(2)
F statistics (Wald test)	5.99	7.71	5.80
	Critical values (T = … 20)
	Lower bounds I(0)	Upper bounds I(1)
1% level of significance	3.74	5.06
5% level of significance	2.86	4.01
10% level of significance	2.45	3.52
Diagnostic tests
$R^2$	0.80	0.85	0.70
F statistics	1.77(0.06)	4.97(0.01)	2.01(0.02)
J-B normality test	1.01(0.63)	0.50(0.79)	1.65(0.43)
Breusch–Godfrey lm test	2.19(0.33)	0.20(1.10)	2.40(0.30)
ARCH test	0.20(0.68)	0.94(0.33)	1.22(0.26)
Ramsey reset test	t = 0.66(0.52)	2.12(0.18)	2.50(0.10)

Notes: The optimal lag length structure is selected by Akaike Information Criterion (AIC).

The *** and ** indicate the significant at 1% and 5% levels, respectively.

Table 4. Long-run results ($\mathrm{I}\mathrm{N}{\mathrm{N}}_{\mathrm{t}}$is dependent variable)

Variables	Coefficient	T—statistics
Constant	−4.09	−0.317902
$\mathrm{U}\mathrm{N}{\mathrm{C}}_{\mathrm{t}}$	−0.107	−1.85*
$\mathrm{G}\mathrm{D}{\mathrm{P}}_{\mathrm{t}}$	0.015	1.83*
Diagnostic tests
$R^2$	0.99
F statistics	1199.397
(${\chi }^2)$J-B normality test	0.38(0.82)
(${\chi }^2)$Breusch–Godfrey lm test	9.18(0.20)
(${\chi }^2)$ARCH test	1.03(0.30)
Durbin-Watson (DW)	2.12
(${\chi }^2)$Ramsey reset test	0.31(0.76)

$\mathbf{N}\mathbf{o}\mathbf{t}\mathbf{e}\mathbf{s}:{\chi }^2$ NORMAL test relates to the Jarque–Bera statistic of the test for normality of data, ${\chi }^2$ the Breusch—Godfrey LM test is related to serial correlation, ${\chi }^2$ ARCH test is the Engle’s test for “autoregressive conditional heteroskedasticity,” and ${\chi }^2$ Ramsey reset test is used to test for model specification test, * represents the 10% level of significance. Durbin–Watson (DW) test is used to find the problem of autocorrelation results to indicate that value of DW is 2.12, indicated that no problem of autocorrelation.

Table 5. Variance decomposition approach

Time horizons	Variance Decomposition of (INNt)			Variance Decomposition of (UNCt)			Variance Decomposition of (GDPt)
	(INNt)	(UNCt)	(GDPt)	(INNt)	(UNCt)	(GDPt)	(INNt)	(UNCt)	(GDPt)
1	100	0.0000	0.0000	9.0865	90.9135	0.0000	7.0636	74.2241	18.7122
2	97.9563	0.11164	1.9321	8.4454	90.8577	0.6970	8.0471	76.0010	15.9518
3	88.8567	7.5179	3.6253	9.7793	89.5254	0.6953	7.9196	76.3479	15.7323
4	71.0677	22.2517	6.6805	9.6207	87.1743	3.2050	7.9141	76.3516	15.7342
5	55.5545	34.9848	9.4606	9.3135	85.3306	5.3558	7.9022	76.3669	15.7308
6	47.3813	42.0059	10.6127	9.0133	85.4487	5.5379	7.9516	76.3214	15.7269
7	43.0178	45.5669	11.4151	9.1316	85.3448	5.5235	7.9736	76.2942	15.7322
8	40.1405	47.8265	12.0326	9.4906	85.0055	5.5038	7.9668	76.2707	15.7623
9	37.8836	49.5705	12.5458	9.6615	84.7911	5.5474	7.9560	76.2747	15.7692
10	36.0746	51.0249	12.9005	9.6808	84.6575	5.6616	7.9576	76.2775	15.7647

Figure 1. Total patent applications in SIPO (1995–2015).

Data source: Chinese invention patent statistics taken from SIPO.

Figure 2. Impulse response function (multiple graphs).