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摘要: 为了揭示内河航运与区域经济间的作用机理, 以上海内河航运为研究对象, 建立了基于Logistic曲线和协整理论的内河航运与区域经济的伴生关系分析模型, 并分别应用生产函数法、主成分分析法、方差分析法和数据包络法测算了内河航运量对GDP的贡献度。计算结果表明: 内河航运与区域经济具有类似的发展规律, 即在发展初期增速很快, 发展到一定阶段后, 增长率逐渐减慢, 直到平稳状态, 两者之间存在着很强的伴生关系; 内河航运对区域经济的贡献度均稳定在2%~4%之间。Abstract: In order to discover the interaction mechanism between inland waterway transport and local economy, taking Shanghai inland waterway transport as an example, their correlation analysis models were built based on logistic curve and cointegration theory.The contribution rate of inland waterway transport to GDP was calculated respectively by using production function theory, principal component analysis, variance analysis and data envelopment analysis.Calculation result shows that inland waterway transport and local economy have similar development regulars and strong associated relationship.The growth rate of early stage increases rapidly, and then gradually slows down until the steady state.The contribution rate of inland waterway transport to local economy is about 2%~4%.
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图 1 内河航运与区域经济关系
Figure 1. Relation between inland waterway transport and local economy
表 1 时间序列的平稳性检验
Table 1. Stationary test of time series
变量 ADF统计值 1%水平临界值 平稳性 一阶差分ADF统计值 1%水平临界值 平稳性 Gt -1.458 147 -3.737 8 非平稳 -3.843 900 -3.765 9 平稳 Ht -2.501 254 -3.737 8 非平稳 -7.935 013 -3.765 9 平稳 
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表 2 两序列间协整关系检验
Table 2. Cointegration test of two series
序列 特征值 似然比检验统计量 5%水平临界值 1%水平临界值 标准化协整方程 Ht与Gt 0.962 273 48.506 25 15.41 20.04 M=Gt-489.599Ht- 1 581.6 0.677 699 12.454 95 3.76 6.65
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表 3 因果关系检验
Table 3. Cause-effect relation test
原假设 F统计量 是否存在因果关系 相伴概率 Ht不是Gt的Granger成因 57.006 80 是 0.000 035 Gt不是Ht的Granger成因 0.301 25 否 0.596 450
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表 4 贡献度测算结果
Table 4. Calculating results of contribution rates
年份 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 贡献度/% 3.55 3.87 3.95 4.02 3.72 5.09 4.31 3.98 4.15 4.03 4.07
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表 5 特征值与特征向量
Table 5. Eigenvalues and eigenvectors
主成分 特征值解释 特征向量值ei1 特征值 贡献度 累积贡献度 1 4.526 92.524 92.524 0.217 (内河货物量) 2 0.313 6.268 0.210 (铁路运输量) 3 0.096 1.914 0.201 (公路运输量) 4 0.063 1.253 0.214 (水运量) 5 0.002 0.042 0.209 (民航运输量)
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表 6 多变量时间序列的平稳性检验
Table 6. Stationary test of multi-variable time series
变量 ADF统计值 1%水平临界值 平稳性 一阶差分ADF统计值 1%水平临界值 平稳性 Gt -1.458 147 -3.737 8 非平稳 -3.843 900 -3.765 9 平稳 Ht -2.501 254 -3.737 8 非平稳 -7.935 013 -3.765 9 平稳 Pt -2.614 317 -3.737 8 非平稳 -7.584 331 -3.765 9 平稳 Yt -3.001 245 -3.737 8 非平稳 -9.754 056 -3.76 59 平稳 Zt -2.557 956 -3.737 8 非平稳 -11.377 610 -3.765 9 平稳
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表 7 无约束协整向量检验与协整方程
Table 7. Unconstrained cointegration vector tests and cointegration formulas
协整方差个数 特征值 似然比统计量 5%水平临界值 Gt Ht Pt Yt Zt 0个 0.984 475 242.730 69.819 -0.012 0 12.577 0 -0.279 4 0.007 8 -0.023 0 至少1个 0.935 906 130.270 47.856 0.000 8 7.208 0 -3.227 0 0.001 1 -0.023 4 至少2个 0.647 863 56.086 29.797 -0.014 5 9.668 0 -35.110 0 0.007 6 -0.006 9 至少3个 0.502 971 27.905 15.495 0.001 4 -3.146 4 -34.957 0 -0.001 8 0.028 3 至少4个 0.284 000 9.029 3.842 -0.021 7 20.828 0 12.273 0 0.009 9 -0.035 0
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表 8 方差分解
Table 8. Variance decompostion
期数 预测误差 Gt Ht Pt Yt Zt 1 0.194 836 100.000 00 0.000 000 0.000 000 0.000 000 0.000 000 2 0.223 124 20.124 34 40.928 600 2.459 095 31.430 770 5.057 198 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 15 12.378 100 49.349 29 4.107 172 3.098 888 10.813 864 32.630 790 16 14.218 330 49.334 48 4.222 494 3.513 290 10.964 046 31.965 690 17 16.460 560 49.234 23 4.229 910 3.002 662 11.332 300 32.200 900
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表 9 有效性评价结果
Table 9. Validity evaluation result
年份 模型1的有效性数值θ1 是否有效 模型2的有效性数值θ2 是否有效 贡献度/% 2000 0.924 7 无效 0.943 5 无效 2.03 2001 0.966 2 无效 0.987 7 无效 2.22 2002 1.000 0 有效 1.000 0 弱有效 2003 0.979 0 无效 1.000 0 有效 2.14 2004 1.000 0 弱有效 1.000 0 有效 2005 0.974 0 无效 0.990 0 无效 2.06 2006 1.000 0 有效 1.000 0 有效 2007 0.987 0 无效 1.000 0 弱有效 2.04
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