Identification of the Evolution Stages of China's General Aviation Industry Based on GFM-VAR and Its Analysis of the Effectiveness of Policy Intensity

doi:10.12005/orms.2018.0258

Abstract

Abstract: In order to reveal the general aviation industry system evolution dynamic and orderly rules, mine phase identification evidence of industry formation and sustainable development in light of the incomplete information and continuous clustering characteristics of industry evolution data, we construct Grey sequence generation-Fisher Model(GFM) based on grey system theory and the theory of optimal segmentation to analyse the identification of general aviation industry evolution stage and use the VAR model to study validity of policy intensity. Using the geometric mean strengthening buffer operator (W) GASBO of grey system theory (position weight), we expand waveform characteristics of evolution data to generate the grey sequence matrix; and determine the general aviation industry evolution orderly progressive stage by the optimal segmentation theory group deviation square and minimum judgment principle; and then, through the empirical research by data of general aviation industry evolution stage in China from 2004 to 2013, we find that the cut-off point of China's general aviation industry evolution is in 2010, which has experienced two stages of start-up and growth; the impulse response function based on VAR model determines that the validity of general aviation industry policy intensity influenced by “forced introduction” and “time lag limit” is weak; grey sequence generation optimal segmentation model in view of policy strength (WGASBO operator) is more effective and feasible.

Key words: general aviation industry, evolution, stage identification, optimal segmentation, grey generated sequence, VAR

摘要： 为揭示通用航空产业系统演化的动态和有序规律,挖掘产业形成和可持续发展的阶段识别证据,针对产业演化数据的信息不完全和连续聚类特性,基于灰色系统理论和最优分割理论构建通用航空产业演进阶段识别的灰色生成序列最优分割模型(Grey sequence generation-Fisher Model,GFM),并运用VAR模型分析通航产业政策强度的有效性。首先运用灰色系统理论的(位置加权)几何平均强化缓冲算子(W)GASBO((Weight)Geometry average strengthening buffer operator)扩张演化数据波形特征并生成灰色序列矩阵;运用最优分割理论的组内离差平方和最小判断原则判定通用航空产业演化的有序递进阶段;然后,通过我国2004~2013年通用航空产业演进阶段的实证研究,发现:我国通用航空产业演化以2010年为分界点,先后经历了初创和成长两个阶段;基于VAR模型的脉冲响应函数确定我国通航产业政策强度受“倒逼出台”、“时滞限制”的影响呈弱有效性;基于政策强度(WGASBO算子)的灰色生成序列最优分割模型更有效、更具有可行性。

关键词: 通用航空产业, 演化, 阶段识别, 最优分割, 灰色生成序列, VAR

CLC Number:

F562
C935

LIU Guo-wei, ZHANG Ting-ting. Identification of the Evolution Stages of China's General Aviation Industry Based on GFM-VAR and Its Analysis of the Effectiveness of Policy Intensity[J]. Operations Research and Management Science, 2018, 27(11): 70-78.

刘国巍,张停停. 基于GFM-VAR的我国通用航空产业演进阶段识别及政策强度有效性分析[J]. 运筹与管理, 2018, 27(11): 70-78.

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