乘用车市场需求预测模型研究
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摘要
改革开放以来,随着我国经济的快速发展,人民生活水平大幅提高,需求也是日趋多元化,尤其是对汽车的需求。为了能够更好的把握汽车消费市场的动向,一些学者开始着手对汽车市场进行预测研究,以便于做出更贴近实际的生产销售计划,更好的满足消费者,实现资源的优化配置。
但是,随着影响世界经济发展的不确定性因素增多,对汽车市场的需求预测也变得越来越复杂,原本的预测方法已经不能很好的表达市场需求的真实动向,这就需要有更加合理的预测方法来对汽车市场的需求做出预测,以适应经济发展。
本文共分为绪论、正文及结束语三部分。绪论部分阐述了本文的研究背景、研究意义及研究现状,介绍了全文的框架结构和研究方法。正文部分首先从分析乘用车市场的发展现状入手,然后深入探讨影响乘用车市场发展的主要因素,接下来,借用研究机构较为成熟的乘用车市场需求预测模型,通过对模型的分析,运用超定求解的方法确定模型的待定系数,使得模型的使用更加方便快捷,而且本文还构建了乘用车市场千人保有量预测的智能系统；最后,对2010年的乘用车市场需求量进行了预测,给出了中国乘用车生产企业应对未来市场的对策建议。结束语部分阐述了本文的研究结果,指出了研究过程中的不足之处,并对下一步研究做了展望。
Since the reform and opening-up, China's market economy construction was outstanding, people's living standards improved significantly. With the great improve of people's living conditions, the demand is various, especially for "car needs"-- This modern transportation is convenient, not only to meet work and live in need, but also caters to people's motivation such as seeking the name of beauty, novelty, and comparisons. With the further development of the reform and opening-up, the demand will greatly influence the development of China's automotive industry.
When the world over to accept the test of the financial crisis, the Chinese automotive industry first to rise ahead, the production of passenger cars reached 10 million level in 2009. China's passenger cars went into the popularity and more and more cars went into the millions of households, the next decade will be the fast development times of China's passenger cars market.
In view of this, the scholars do a lot of research in the cars'market demand forecasting. Being relevant to passenger cars market demand research about quantitative or qualitative, most were common in from the clustering analysis, Logistic curve model, gray theory, the system dynamics theory, combined forecasting theory etc. Due to the characteristics of itself, the prediction results can only do relatively accurate, along with the change of the internal and external environments, some original forecasting results also become relatively inaccurate, which need to considerate the change comprehensively of the internal and external environment factors, and the original method for forecasting need to improve, or use the new forecasting methods.
The regional development of China's passenger cars'market is not balanced. Regional Economic Development Theory said that the development process of regional economic takes on different stages. Although China's car market continue leading global sales, car sales retaining its position as the world's first, but the China's situation determines its regional development imbalance. According to the statistics, by 2005,16 percent of the passenger cars sales concentrated in Beijing, Shanghai and Guangzhou, a regional economic center and super-developed Yangtze River Delta, Pearl River Delta region accounted for 19 percent, middle income's capitals and more developed prefecture-level cities accounted for 18 percent, the eastern regions accounted for 64 percent. Within the industry, based on the various regional economic development level, selected three indicators--the city population, per capita GDP and passenger demand level of districts, according to the geographical distribution (the eastern, the central, the west and the north-east), divided the National 287-level cities into five regional classification. However, in the specific dividing line, due to the number of cities increased, according to the economic development level and local passenger vehicle license plate volume, the author adjusted the classification standard, divided the National 348 places above the county level into six regional classification, such zoning become more detailed, which is in favor of grasping market well, and making a regional demand forecasting.
With the development of economy in China, the influence factors of the car market development also changes. In this paper, the author uses PEST analysis to analyze the macro environment of China passenger cars'market. In order to cope with the global financial crisis, in 2009, the government promulgated most of policy measures to develop the cars'market, and with the rapid growth of GDP, per capita disposable income increased relatively, purchasing power enhanced, the needs of potential auto consumer groups were strong, the M&A of automobile industry, technical innovation also reduced the purchasing cost and using cost.
In addition, along with the continuous development of regional economy and regional market has a huge potential for development. According to the theory of regional economic passage, within a certain range, the economic and technological development level is not balanced, there will exist a certain degree of economic and technological gradient difference, and will form the space passage of productivity. China's economic geographically objectively exists three gradient differences in the holdings for thousands of people, we can make demand forecasting next some years. Therefore, the author constructs an intelligent functions system module of passenger cars'market demand forecasting.
Finally, combined with the actual data of China's passenger cars'market by 2010, demand of relativity passenger cars'market is forecasted., and puts forward some strategies:1. Make full use of government policies, and achieve flourish of production and sale; 2. Increase the technical innovation, and improve product value added; 3. Shape automobile consumption culture, and guide consumer restored consumer confidence gradually; 4. Construct market research team, and increase the market research investment, and improve the market forecast.
但是,随着影响世界经济发展的不确定性因素增多,对汽车市场的需求预测也变得越来越复杂,原本的预测方法已经不能很好的表达市场需求的真实动向,这就需要有更加合理的预测方法来对汽车市场的需求做出预测,以适应经济发展。
本文共分为绪论、正文及结束语三部分。绪论部分阐述了本文的研究背景、研究意义及研究现状,介绍了全文的框架结构和研究方法。正文部分首先从分析乘用车市场的发展现状入手,然后深入探讨影响乘用车市场发展的主要因素,接下来,借用研究机构较为成熟的乘用车市场需求预测模型,通过对模型的分析,运用超定求解的方法确定模型的待定系数,使得模型的使用更加方便快捷,而且本文还构建了乘用车市场千人保有量预测的智能系统；最后,对2010年的乘用车市场需求量进行了预测,给出了中国乘用车生产企业应对未来市场的对策建议。结束语部分阐述了本文的研究结果,指出了研究过程中的不足之处,并对下一步研究做了展望。
Since the reform and opening-up, China's market economy construction was outstanding, people's living standards improved significantly. With the great improve of people's living conditions, the demand is various, especially for "car needs"-- This modern transportation is convenient, not only to meet work and live in need, but also caters to people's motivation such as seeking the name of beauty, novelty, and comparisons. With the further development of the reform and opening-up, the demand will greatly influence the development of China's automotive industry.
When the world over to accept the test of the financial crisis, the Chinese automotive industry first to rise ahead, the production of passenger cars reached 10 million level in 2009. China's passenger cars went into the popularity and more and more cars went into the millions of households, the next decade will be the fast development times of China's passenger cars market.
In view of this, the scholars do a lot of research in the cars'market demand forecasting. Being relevant to passenger cars market demand research about quantitative or qualitative, most were common in from the clustering analysis, Logistic curve model, gray theory, the system dynamics theory, combined forecasting theory etc. Due to the characteristics of itself, the prediction results can only do relatively accurate, along with the change of the internal and external environments, some original forecasting results also become relatively inaccurate, which need to considerate the change comprehensively of the internal and external environment factors, and the original method for forecasting need to improve, or use the new forecasting methods.
The regional development of China's passenger cars'market is not balanced. Regional Economic Development Theory said that the development process of regional economic takes on different stages. Although China's car market continue leading global sales, car sales retaining its position as the world's first, but the China's situation determines its regional development imbalance. According to the statistics, by 2005,16 percent of the passenger cars sales concentrated in Beijing, Shanghai and Guangzhou, a regional economic center and super-developed Yangtze River Delta, Pearl River Delta region accounted for 19 percent, middle income's capitals and more developed prefecture-level cities accounted for 18 percent, the eastern regions accounted for 64 percent. Within the industry, based on the various regional economic development level, selected three indicators--the city population, per capita GDP and passenger demand level of districts, according to the geographical distribution (the eastern, the central, the west and the north-east), divided the National 287-level cities into five regional classification. However, in the specific dividing line, due to the number of cities increased, according to the economic development level and local passenger vehicle license plate volume, the author adjusted the classification standard, divided the National 348 places above the county level into six regional classification, such zoning become more detailed, which is in favor of grasping market well, and making a regional demand forecasting.
With the development of economy in China, the influence factors of the car market development also changes. In this paper, the author uses PEST analysis to analyze the macro environment of China passenger cars'market. In order to cope with the global financial crisis, in 2009, the government promulgated most of policy measures to develop the cars'market, and with the rapid growth of GDP, per capita disposable income increased relatively, purchasing power enhanced, the needs of potential auto consumer groups were strong, the M&A of automobile industry, technical innovation also reduced the purchasing cost and using cost.
In addition, along with the continuous development of regional economy and regional market has a huge potential for development. According to the theory of regional economic passage, within a certain range, the economic and technological development level is not balanced, there will exist a certain degree of economic and technological gradient difference, and will form the space passage of productivity. China's economic geographically objectively exists three gradient differences in the holdings for thousands of people, we can make demand forecasting next some years. Therefore, the author constructs an intelligent functions system module of passenger cars'market demand forecasting.
Finally, combined with the actual data of China's passenger cars'market by 2010, demand of relativity passenger cars'market is forecasted., and puts forward some strategies:1. Make full use of government policies, and achieve flourish of production and sale; 2. Increase the technical innovation, and improve product value added; 3. Shape automobile consumption culture, and guide consumer restored consumer confidence gradually; 4. Construct market research team, and increase the market research investment, and improve the market forecast.
引文
1参见广西电视网财经新闻(2010-01-08),数据来源于网易财经。
2数据来源于《2009年中国统计年鉴》。
1摘自《汽车产业调整和振兴规划》,国务院办公厅,2009年3月20日。
[1]焦海斌,王冬,杨宁江,沈理.“八五”末期我国各地区汽车市场总体需求状况聚类分析初探[J].汽车工业研究,1997(2)：26-30.
[2]汽车行业“十五”规划研究课题组.“十五”汽车市场需求预测[J].机电新产品导报,2001(1—2)：50-57.
[3]“十五”汽车市场需求预测[J].中国橡胶,第18卷第17期：7-8.
[4]焦海斌.1995-2005年我国汽车市场需求预测[J].汽车工业研究,1994(9)：28-33.
[5]尚顺晓.1996年我国轿车市场评析与1997年中级轿车市场预测[J].市场研究,1996(11)：40-44.
[6]华钢,张乾,周明生.2001年汽车市场需求预测[J].汽车科技,2001(2)：6-9.
[7]王晓红.2005年汽车市场需求预测[J].行业研究,2005(3)：21-28.
[8]王晶.2007年上半年中国汽车市场回顾及下半年展望[J].营销服务,2007(9)：32-36.
[9]喻小军,杨双会,谭建.2010-2020年我国轿车市场需求预测分析[J].武汉理工大学学报,2008(6)：163-166.
[10]罗廷发.产品差异化视角下的中国家庭轿车市场研究[D].浙江：浙江大学管理学院,2007.
[11]祝田田.城镇化进程中微型汽车市场需求发展趋势研究[D].重庆：重庆大学机械工程学院,2004.
[12]冯凤玲,胡树华。城镇居民收入对汽车消费市场影响的研究[J].商业时代,2007(5)：82-83.
[13]崔光炜.对家用汽车的特征及其消费者偏好的研究[D].北京：对外经济贸易大学国际商学院,2006.
[14]王晓峰.对我国家用汽车消费群体及其消费特征的社会性分析[D].吉林：吉林大学哲学社会学院,2008.
[15]张正智.国家应出台支持小排量乘用车发展的产业政策(三)[J].汽车工业研究,2008(2)：2-8.
[16]陈欢.灰色理论在汽车销售预测和投资决策中的应用研究[D].安徽：合肥工业大学管理学院,2008.
[17]蔡家明.灰色系统模型在汽车市场需求预测中的应用[J].上海工程技术大学学报,2003(3)：72-74.
[18]朱翠珊.基于价值体系模型的乘用车消费者需求研究[D].上海：东华大学旭日工商管理学院,2005.
[19]仲伟周,王军.基于因子分析法的中国民用汽车保有量影响因素分析及其政策含义[J].统计与信息论坛,2008(11)：16-20.
[20]杨秦,付正伦.牛年车市：小型车风光独好[N].中国经济导报,2009(1)：1-3
[21]杨玉峨.奇瑞公司短期汽车需求预测分析[D].陕西：西北大学经济管理学院,2003.
[22]马超群,赵海龙.汽车市场需求预测建模及其应用研究[J].湖南大学学报(社会科学版),2009(7)：38-44.
[23]刘瑞年.汽车销售预测模型应用研究[D].湖北：武汉理工大学管理学院,2009.
[24]胡伟.我国汽车销售量预测研究[D].江苏：河海大学商学院,2007.
[25]孙绍惠.影响国产轿车需求因素的研究[D].黑龙江：哈尔滨工程大学经济管理学院,2003.
[26]郭维春.中国居民收入分布对乘用车市场影响的动态仿真模型研究[D].吉林：吉林大学管理学院,2007.
[27]赵海龙.中国汽车保有量预测建模及其应用研究[D].湖南：湖南大学工商管理学院,2009.
[28]余学东.中国汽车工业发展对GDP的影响研究以及市场需求预测[D].北京：清华大学经济管理学院,2004.
[29]邓丽娜.中国汽车工业与国民经济发展的相关分析及需求预测[D].四川：西南交通大学经济管理学院,2005.
[30]潘志刚,韩颖.组合预测法在我国汽车市场需求预测中的应用[J].商业研究,2006(2)：126-129.
[31]赵韩,许辉,梁平,陈传魁,陈欢.最优组合预测方法在家用汽车需求预测中的应用[J].工业工程,2008(1)：126-128.
[32]MogridgeM JH. The Prediction of car Ownership [J]. Journal of Transport Economics and Policy,1967(1):52-75.
[33]Engle R. F. and GrangerC.W. J. Co-integration and error correction: representation, estimation and testing[J]. Econometrica,1987(2):251-276.
[34]Vapnik V. The Nature of Statistical Learning Theory[M]. New York:Springer Verlag,1995.
[35]Schlkopf B, Smola A J. A Tutorial on Support Vector Regression[R]. London: Royal Holloway College, University of London,1998.
[36]Kim K J. Financial Time Series Forecasting Using Support Vector Machine [J].Neurocomputing,2003,55(1):307-319.
[37]Muller K R, Smola A J, Ratsch G, et al. Predicting Time Series with Support Vector Machines[A]. In:Gerstner W, Germond A, Hasler M, Nicoud J D eds. Proceedings of ICANN'97[C].Berlin:Springer Lecture Notes in Computer Science,1997:999-1005.
[38]Suykens J A K, Vandewalle J. Least Squares Support Vector Machines Classifiers[J]. Neural Network Letters,1999,19(3):293-300.
[39]HongZhang, ZhuguoLi, ZhaonengChen. Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines[J]. Tribology International,36(2003),753-56.
[40]G. Simon, et al. Time series forecasting:Obtaining long term trends with self-organizing maps[J]. Pattern Recognition Letters,2005(12):1795-1808.
2数据来源于《2009年中国统计年鉴》。
1摘自《汽车产业调整和振兴规划》,国务院办公厅,2009年3月20日。
[1]焦海斌,王冬,杨宁江,沈理.“八五”末期我国各地区汽车市场总体需求状况聚类分析初探[J].汽车工业研究,1997(2)：26-30.
[2]汽车行业“十五”规划研究课题组.“十五”汽车市场需求预测[J].机电新产品导报,2001(1—2)：50-57.
[3]“十五”汽车市场需求预测[J].中国橡胶,第18卷第17期：7-8.
[4]焦海斌.1995-2005年我国汽车市场需求预测[J].汽车工业研究,1994(9)：28-33.
[5]尚顺晓.1996年我国轿车市场评析与1997年中级轿车市场预测[J].市场研究,1996(11)：40-44.
[6]华钢,张乾,周明生.2001年汽车市场需求预测[J].汽车科技,2001(2)：6-9.
[7]王晓红.2005年汽车市场需求预测[J].行业研究,2005(3)：21-28.
[8]王晶.2007年上半年中国汽车市场回顾及下半年展望[J].营销服务,2007(9)：32-36.
[9]喻小军,杨双会,谭建.2010-2020年我国轿车市场需求预测分析[J].武汉理工大学学报,2008(6)：163-166.
[10]罗廷发.产品差异化视角下的中国家庭轿车市场研究[D].浙江：浙江大学管理学院,2007.
[11]祝田田.城镇化进程中微型汽车市场需求发展趋势研究[D].重庆：重庆大学机械工程学院,2004.
[12]冯凤玲,胡树华。城镇居民收入对汽车消费市场影响的研究[J].商业时代,2007(5)：82-83.
[13]崔光炜.对家用汽车的特征及其消费者偏好的研究[D].北京：对外经济贸易大学国际商学院,2006.
[14]王晓峰.对我国家用汽车消费群体及其消费特征的社会性分析[D].吉林：吉林大学哲学社会学院,2008.
[15]张正智.国家应出台支持小排量乘用车发展的产业政策(三)[J].汽车工业研究,2008(2)：2-8.
[16]陈欢.灰色理论在汽车销售预测和投资决策中的应用研究[D].安徽：合肥工业大学管理学院,2008.
[17]蔡家明.灰色系统模型在汽车市场需求预测中的应用[J].上海工程技术大学学报,2003(3)：72-74.
[18]朱翠珊.基于价值体系模型的乘用车消费者需求研究[D].上海：东华大学旭日工商管理学院,2005.
[19]仲伟周,王军.基于因子分析法的中国民用汽车保有量影响因素分析及其政策含义[J].统计与信息论坛,2008(11)：16-20.
[20]杨秦,付正伦.牛年车市：小型车风光独好[N].中国经济导报,2009(1)：1-3
[21]杨玉峨.奇瑞公司短期汽车需求预测分析[D].陕西：西北大学经济管理学院,2003.
[22]马超群,赵海龙.汽车市场需求预测建模及其应用研究[J].湖南大学学报(社会科学版),2009(7)：38-44.
[23]刘瑞年.汽车销售预测模型应用研究[D].湖北：武汉理工大学管理学院,2009.
[24]胡伟.我国汽车销售量预测研究[D].江苏：河海大学商学院,2007.
[25]孙绍惠.影响国产轿车需求因素的研究[D].黑龙江：哈尔滨工程大学经济管理学院,2003.
[26]郭维春.中国居民收入分布对乘用车市场影响的动态仿真模型研究[D].吉林：吉林大学管理学院,2007.
[27]赵海龙.中国汽车保有量预测建模及其应用研究[D].湖南：湖南大学工商管理学院,2009.
[28]余学东.中国汽车工业发展对GDP的影响研究以及市场需求预测[D].北京：清华大学经济管理学院,2004.
[29]邓丽娜.中国汽车工业与国民经济发展的相关分析及需求预测[D].四川：西南交通大学经济管理学院,2005.
[30]潘志刚,韩颖.组合预测法在我国汽车市场需求预测中的应用[J].商业研究,2006(2)：126-129.
[31]赵韩,许辉,梁平,陈传魁,陈欢.最优组合预测方法在家用汽车需求预测中的应用[J].工业工程,2008(1)：126-128.
[32]MogridgeM JH. The Prediction of car Ownership [J]. Journal of Transport Economics and Policy,1967(1):52-75.
[33]Engle R. F. and GrangerC.W. J. Co-integration and error correction: representation, estimation and testing[J]. Econometrica,1987(2):251-276.
[34]Vapnik V. The Nature of Statistical Learning Theory[M]. New York:Springer Verlag,1995.
[35]Schlkopf B, Smola A J. A Tutorial on Support Vector Regression[R]. London: Royal Holloway College, University of London,1998.
[36]Kim K J. Financial Time Series Forecasting Using Support Vector Machine [J].Neurocomputing,2003,55(1):307-319.
[37]Muller K R, Smola A J, Ratsch G, et al. Predicting Time Series with Support Vector Machines[A]. In:Gerstner W, Germond A, Hasler M, Nicoud J D eds. Proceedings of ICANN'97[C].Berlin:Springer Lecture Notes in Computer Science,1997:999-1005.
[38]Suykens J A K, Vandewalle J. Least Squares Support Vector Machines Classifiers[J]. Neural Network Letters,1999,19(3):293-300.
[39]HongZhang, ZhuguoLi, ZhaonengChen. Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines[J]. Tribology International,36(2003),753-56.
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