Industrial Agglomeration Analysis Based on Spatial Durbin Model: Evidence from Beijing-Tianjin-Hebei Economic Circle in China

Table 3

Empirical results of dynamic spatial Durbin model.

Variable	Geographical distance weight matrix	Economic distance weight matrix
Yt−1	0.125	0.143
	(0.043)	(0.045)
lnX1	0.676	0.269
	(0.127)	(0.111)
lnX2	0.275	0.113
	(0.049)	(0.053)
lnX1_lnX2	−0.062	−0.022
	(−0.013)	(−0.011)
Z1	−0.736	−0.634
	(−0.301)	(−0.335)
Z2	1.526	0.537
	(3.014)	(2.667)
Z3	0.098	0.065
	(0.096)	(0.148)
× Yt−1	0.353	0.313
	(0.151)	(0.145)
× lnX1	1.307	−0.141
	(0.447)	(−0.212)
× lnX2	0.604	0.055
	(0.215)	(0.072)
× lnX1_lnX2	−0.174	0.127
	(−0.059)	(0.053)
× Z1	−2.546	−0.044
	(−1.085)	(−0.396)
× Z2	−36.798	−21.406
	(−9.986)	(−10.127)
× Z3	2.376	1.390
	(0.408)	(0.405)
Spatial rho	0.872	−0.592
	(0.260)	(−0.166)
N	120	120
Log-likelihood	99.6653	88.3828
R^2	0.826	0.783

Note. , , , and indicate significant differences at 10%, 5%, and 1% test levels, respectively.