企业景气指数的干预模型研究
|
摘要
企业景气指数能够比较科学的反映现阶段企业的运行状况并判断未来经济发展的态势,能够迅速、可靠地了解和预测(短期)宏观经济的运行情况,是跟踪经济周期性变动、确定经济转折点、建立经济预测系统的一种有效手段。目前,企业景气指数已成为监测宏观经济发展趋势及企业生产经营状况的重要手段之一,因此企业景气指数的研究具有重要的意义。
本文首先对企业景气指数序列进行数据处理,建立比较合理的干预模型对其进行预测,根据预测结果来判断经济的发展状况；接着建立企业景气指数与GDP季度累计增长率之间的关系模型,利用企业景气指数较为准确的预测值对GDP季度累计增长率进行短期预测,从GDP方面来说明经济的发展状况。
最后得出以下结论：
1.作为一个时间序列,企业景气指数序列可以利用时间序列分析方法进行研究。在只有一个干预事件(金融危机)的情况下,可以建立合适的单变量干预模型,并利用此模型对企业景气指数进行预测,通过验证,预测结果很准确。
2.根据企业景气指数的预测结果可知,在没有其它重大干预的情况下,短期经济会呈现出稳速的发展景象。政府部门可以此作为制定宏观经济政策的决策依据,企业经营管理者也可以此作为制订企业发展战略和经营计划的重要参考。
3.可以建立一元线性回归模型,利用企业景气指数预测数据对GDP季度累计增长率进行短期预测,预测结果可信度比较高。
4.通过GDP季度累计增长率的预测值可以看出,短期内经济发展会呈现出较快的势头。我们也可以推断出2011年我国GDP年度增长率还会高于8%的设定目标。
Enterprise prosperity index can scientifically reflect the current enterprises running situation and estimate the future economic development trend. It can find out and predict (short-term) macroeconomic operation quickly and reliably. It is an effective means of tracking economic cyclical fluctuations, revealing the economic turning point and establishing economic forecast system. At present, enterprise prosperity index has become one of the important methods for monitoring macroeconomic development trend and the enterprises running situation. So the research of enterprise prosperity index has an important significance.
In this paper, we have been studied enterprise prosperity index from the perspective of time series analysis. First, the series enterprise prosperity index has been processed. Then, a reasonable intervention model is established in order to predict enterprise prosperity index. According to the forecast results we determine the development of economy condition. Finally, we build the relation model between enterprise prosperity index and GDP quarterly accumulative total growth rate. Through this model, we can use enterprise prosperity index to predict the GDP quarterly accumulative total growth rate for short-term forecasting and then we can estimate the development situation of the economy from the aspects of GDP.
The main content and conclusions of this research are as follows:
1. As a time series, the enterprise prosperity index series can be studied using time series analysis method. In this example, only one intervention events (financial crisis) has an effect on enterprise prosperity index. Under this kind of circumstance, we establish a suitable univariate intervention model and use this model to forecast Enterprise prosperity index. Through the verification, the prediction is accurate.
2. According to forecasting results of Enterprise prosperity index, we learn that in normal circumstances the short-term economy appears a steady speed development vision. Government departments can use them as decision-making basis to make out macroeconomic policies. The management of the enterprise managers can also use them as an important reference to make out enterprise development strategies and business plans.
3. Using the enterprise prosperity index, we can set unitary linear regression model to predict GDP quarterly cumulative total growth rate for short-term forecasting. The credibility of forecasting results is quite high.
4. The forecasting results of GDP quarterly cumulative total growth rate shows that in normal circumstances the short-term economy appears a faster momentum. We can also infer that in 2011 the annual growth in GDP will be more than 8 percent in setting goals.
本文首先对企业景气指数序列进行数据处理,建立比较合理的干预模型对其进行预测,根据预测结果来判断经济的发展状况；接着建立企业景气指数与GDP季度累计增长率之间的关系模型,利用企业景气指数较为准确的预测值对GDP季度累计增长率进行短期预测,从GDP方面来说明经济的发展状况。
最后得出以下结论：
1.作为一个时间序列,企业景气指数序列可以利用时间序列分析方法进行研究。在只有一个干预事件(金融危机)的情况下,可以建立合适的单变量干预模型,并利用此模型对企业景气指数进行预测,通过验证,预测结果很准确。
2.根据企业景气指数的预测结果可知,在没有其它重大干预的情况下,短期经济会呈现出稳速的发展景象。政府部门可以此作为制定宏观经济政策的决策依据,企业经营管理者也可以此作为制订企业发展战略和经营计划的重要参考。
3.可以建立一元线性回归模型,利用企业景气指数预测数据对GDP季度累计增长率进行短期预测,预测结果可信度比较高。
4.通过GDP季度累计增长率的预测值可以看出,短期内经济发展会呈现出较快的势头。我们也可以推断出2011年我国GDP年度增长率还会高于8%的设定目标。
Enterprise prosperity index can scientifically reflect the current enterprises running situation and estimate the future economic development trend. It can find out and predict (short-term) macroeconomic operation quickly and reliably. It is an effective means of tracking economic cyclical fluctuations, revealing the economic turning point and establishing economic forecast system. At present, enterprise prosperity index has become one of the important methods for monitoring macroeconomic development trend and the enterprises running situation. So the research of enterprise prosperity index has an important significance.
In this paper, we have been studied enterprise prosperity index from the perspective of time series analysis. First, the series enterprise prosperity index has been processed. Then, a reasonable intervention model is established in order to predict enterprise prosperity index. According to the forecast results we determine the development of economy condition. Finally, we build the relation model between enterprise prosperity index and GDP quarterly accumulative total growth rate. Through this model, we can use enterprise prosperity index to predict the GDP quarterly accumulative total growth rate for short-term forecasting and then we can estimate the development situation of the economy from the aspects of GDP.
The main content and conclusions of this research are as follows:
1. As a time series, the enterprise prosperity index series can be studied using time series analysis method. In this example, only one intervention events (financial crisis) has an effect on enterprise prosperity index. Under this kind of circumstance, we establish a suitable univariate intervention model and use this model to forecast Enterprise prosperity index. Through the verification, the prediction is accurate.
2. According to forecasting results of Enterprise prosperity index, we learn that in normal circumstances the short-term economy appears a steady speed development vision. Government departments can use them as decision-making basis to make out macroeconomic policies. The management of the enterprise managers can also use them as an important reference to make out enterprise development strategies and business plans.
3. Using the enterprise prosperity index, we can set unitary linear regression model to predict GDP quarterly cumulative total growth rate for short-term forecasting. The credibility of forecasting results is quite high.
4. The forecasting results of GDP quarterly cumulative total growth rate shows that in normal circumstances the short-term economy appears a faster momentum. We can also infer that in 2011 the annual growth in GDP will be more than 8 percent in setting goals.
引文
1. http://www.stats.gov.cn/tjsj/jidusj/国家统计局网站
2.蔡基栋,冯文权.政策干预对经济影响的侧算方法[J].武汉大学学报(社会科学版).1993(2)：56-64
3.陈磊.企业景气状况与宏观经济运行[J].管理世界,2004(3)：14-24
4.戴晓枫,肖庆宪.美联储升息对外汇市场影响的干预分析模型[J].数学的实践与认识2005,35(4)：82-88
5.冯文权.干预分析模型及其应用[J].预测,1989(6)：52-56
6.冯文权.传递函数模型与干预分析模型[J].经济技术经济研究,1989,(12)：47-53
7.冯文权.政策干预或突发事件影响的统计分析模型[J].统计与决策,1990,(6)：8-11
8.冯文权,蔡基栋.干预分析模型及其应用[J].武汉大学学报(自然科学版),1994(6)：29-35
9.冯文权.经济预测与决策技术[M].武昌：武汉大学出版社,1994：176-181
10.高铁梅,孔宪丽,刘玉.中国钢铁工业景气指数的开发与应用研究[J].中国工业经济,2003(11)：71-77
11.何荆,尚国强.企业景气调查与GDP增长率相关关系及其预测研究[J].浙江统计改革论坛,2003(3)：14-16
12.黄德军.用主成分分析法编制综合景气指数[J].统计教育,2005(2)：16-17
13.黄玉林,沈长泗.济南夏季降水量数据的初步统计分析[J].山东轻工业学院学报,2003,17(1)：79-82
14.孔杰.怎样认识企业景气指数[J].中国统计,2007,(2)
15.赖福平.工业企业景气指数研究与实证分析[D].暨南大学,2005.5
16.刘畅,曹延明,马国巍.政策对我国农产品出口影响的干预分析模型-以黑龙江省农产品出口为例[J].苏州大学学报(哲学社会科学版).2008,(6)：43-45
17.刘朝阳,赵晓丽.四个景气指数预测效果的实证检验及政策建议.理论探讨,2007(5)：11-12
18.路群.中国央行降息对股市影响的一类干预分析模型[J].预测,2001(4)：23-27
19.美瑛.财政支出预测中的干预分析[J].北方经济,1999(11)：29-30
20.倪晓莉,陈思路,颜虹.网络依赖行为研究及其干预模型的理论构建[J].兰州大学学报(社会科学版),2003,35(2)：42-46
21.芮执多.国家物资储备局市场运作的干预模型分析[J].上海经济研究,2009(5)：39-47
22.宋晓梅.从企业景气指数看北京经济发展潜在问题[J].北京统计,2004(6)：27-28
23.田金方,张小斐.干预ARIMA模型及其在我国人口总量预测中的实证研究[J].数理统计与管理,2007,27(2)：263-267
24.万平Intervention-ARIMA模型在我国第三产业就业人数预测中的应用[J].统计教育,2009(9)：31-33,37
25.王耀,韩福林,张冬晓.对包头市企业景气指数与GDP相关关系的数量分析[J].数理统计,2003(5)：108
26.王化琨.北京市企业景气波动的实证分析[J].数学的实践与认识,2007,37(2)：45-48
27.王亮萍.从景气指数与GDP的相关分析看景气调查的科学实用性[J].山西统计,2003(10)：10-11
28.王庆露,葛虹.基于协整理论和干预分析的中国电力需求预测[J].数理统计与管理,2007,26(5)：753-758
29.王振龙,胡永宏.应用时间序列分析[M].北京：科学出版社,2007
30.肖枝洪,郭明月.时间序列分析与SAS应用[M].武汉：武汉大学出版社,2009
31.谢向前.中国钢铁产品市场价格干预分析模型研究[J].武汉冶金管理干部学院学报,2006,16(3)：8-11
32.熊焰,余石.干预分析的ARMAX模型及应用[J].统计与决策,2003(11)：17-26
33.杨立,常巍.干预分析模型在中国GDP预测中的应用[J].经济研究刊,2009(1)：7-8
34.杨楠,邢力聪.干预分析模型在房价指数预测中的应用[J].决策参考,2005(11)：51-52
35.张玲,陈军才.干预模型在旅游需求预测中的运用[J].江苏商论,2007(5)：81-83
36.张瑞彬.我国国民经济增长的政策干预分析[J].统计与预测,1997：28-30
37.赵光娟、肖枝洪.《历年中国运动员获世界冠军数的时序分析》,湖北师范学院报 (自然科学版),2009第3期：95-98.
38.赵光娟、肖枝洪.《消费者信心指数的干预模型》,黄冈师范学院报,2010.6第3期：64-68.
39.郑雨尧.抽象市场经济的政府干预模型浅析[J].经济师,2001(9)：68-69
40.周忠彬,吕红梅,邹郢ARIMA干预模型在医院门诊量预测中的应用[J].中国医院统计,2008,15(2)：110-112
41. Abraham B. Intervention analysis and multiple time series[J]. Biome trika,1980,67: 73-78
42. Acziel A D. Multivariate intervention analysis[J]. Statistical Aplicata,1992(4): 735-739
43. Barros C P. An intervention analysis of terrorism:the Spanish ETA case[J], Defence and Peace Economics,2003,14(6):401-402
44. Box GEP, Jenkins GM, Reinsel GC. Time series analysis:forecasting and control(3rdEd.)[M],英文影印版,人民邮电出版社,2005：462-480
45. Box G E P, Tiao G C, Intervention analysis with application to economic and environmental problems[J]. Journal of the American Statistical Association,1975, 70:70-79
46. Bruce L. Bowerman, Richard T.o'Connell. Forecasting and Time Series:An Applied Approach[M]. Beijing:Mechanical Industry Press(3rd Ed.),2003,7:657-694
47. Geoge E P, Box, Gwilym M.Jenkins. Time Series Analysis:forecasting and control [M],San Francisco,1976
48. Murtaugh P A. On rejection rates of paired intervention analysis[J]. Technical Report Series,2001 July 25
49. Murtaugh P A. Paired intervention analysis in ecology[J]. Journal of Agricultural: Biological and Environmental Statistics,2005,5(3):280-292.
50. Upali A A, Markandu A. Intervention analysis for impact assessment of irrigation systems. working paper, International Water Management Institute, P.O.Box 2075, Colombo, Sri Lanka, P27-49
2.蔡基栋,冯文权.政策干预对经济影响的侧算方法[J].武汉大学学报(社会科学版).1993(2)：56-64
3.陈磊.企业景气状况与宏观经济运行[J].管理世界,2004(3)：14-24
4.戴晓枫,肖庆宪.美联储升息对外汇市场影响的干预分析模型[J].数学的实践与认识2005,35(4)：82-88
5.冯文权.干预分析模型及其应用[J].预测,1989(6)：52-56
6.冯文权.传递函数模型与干预分析模型[J].经济技术经济研究,1989,(12)：47-53
7.冯文权.政策干预或突发事件影响的统计分析模型[J].统计与决策,1990,(6)：8-11
8.冯文权,蔡基栋.干预分析模型及其应用[J].武汉大学学报(自然科学版),1994(6)：29-35
9.冯文权.经济预测与决策技术[M].武昌：武汉大学出版社,1994：176-181
10.高铁梅,孔宪丽,刘玉.中国钢铁工业景气指数的开发与应用研究[J].中国工业经济,2003(11)：71-77
11.何荆,尚国强.企业景气调查与GDP增长率相关关系及其预测研究[J].浙江统计改革论坛,2003(3)：14-16
12.黄德军.用主成分分析法编制综合景气指数[J].统计教育,2005(2)：16-17
13.黄玉林,沈长泗.济南夏季降水量数据的初步统计分析[J].山东轻工业学院学报,2003,17(1)：79-82
14.孔杰.怎样认识企业景气指数[J].中国统计,2007,(2)
15.赖福平.工业企业景气指数研究与实证分析[D].暨南大学,2005.5
16.刘畅,曹延明,马国巍.政策对我国农产品出口影响的干预分析模型-以黑龙江省农产品出口为例[J].苏州大学学报(哲学社会科学版).2008,(6)：43-45
17.刘朝阳,赵晓丽.四个景气指数预测效果的实证检验及政策建议.理论探讨,2007(5)：11-12
18.路群.中国央行降息对股市影响的一类干预分析模型[J].预测,2001(4)：23-27
19.美瑛.财政支出预测中的干预分析[J].北方经济,1999(11)：29-30
20.倪晓莉,陈思路,颜虹.网络依赖行为研究及其干预模型的理论构建[J].兰州大学学报(社会科学版),2003,35(2)：42-46
21.芮执多.国家物资储备局市场运作的干预模型分析[J].上海经济研究,2009(5)：39-47
22.宋晓梅.从企业景气指数看北京经济发展潜在问题[J].北京统计,2004(6)：27-28
23.田金方,张小斐.干预ARIMA模型及其在我国人口总量预测中的实证研究[J].数理统计与管理,2007,27(2)：263-267
24.万平Intervention-ARIMA模型在我国第三产业就业人数预测中的应用[J].统计教育,2009(9)：31-33,37
25.王耀,韩福林,张冬晓.对包头市企业景气指数与GDP相关关系的数量分析[J].数理统计,2003(5)：108
26.王化琨.北京市企业景气波动的实证分析[J].数学的实践与认识,2007,37(2)：45-48
27.王亮萍.从景气指数与GDP的相关分析看景气调查的科学实用性[J].山西统计,2003(10)：10-11
28.王庆露,葛虹.基于协整理论和干预分析的中国电力需求预测[J].数理统计与管理,2007,26(5)：753-758
29.王振龙,胡永宏.应用时间序列分析[M].北京：科学出版社,2007
30.肖枝洪,郭明月.时间序列分析与SAS应用[M].武汉：武汉大学出版社,2009
31.谢向前.中国钢铁产品市场价格干预分析模型研究[J].武汉冶金管理干部学院学报,2006,16(3)：8-11
32.熊焰,余石.干预分析的ARMAX模型及应用[J].统计与决策,2003(11)：17-26
33.杨立,常巍.干预分析模型在中国GDP预测中的应用[J].经济研究刊,2009(1)：7-8
34.杨楠,邢力聪.干预分析模型在房价指数预测中的应用[J].决策参考,2005(11)：51-52
35.张玲,陈军才.干预模型在旅游需求预测中的运用[J].江苏商论,2007(5)：81-83
36.张瑞彬.我国国民经济增长的政策干预分析[J].统计与预测,1997：28-30
37.赵光娟、肖枝洪.《历年中国运动员获世界冠军数的时序分析》,湖北师范学院报 (自然科学版),2009第3期：95-98.
38.赵光娟、肖枝洪.《消费者信心指数的干预模型》,黄冈师范学院报,2010.6第3期：64-68.
39.郑雨尧.抽象市场经济的政府干预模型浅析[J].经济师,2001(9)：68-69
40.周忠彬,吕红梅,邹郢ARIMA干预模型在医院门诊量预测中的应用[J].中国医院统计,2008,15(2)：110-112
41. Abraham B. Intervention analysis and multiple time series[J]. Biome trika,1980,67: 73-78
42. Acziel A D. Multivariate intervention analysis[J]. Statistical Aplicata,1992(4): 735-739
43. Barros C P. An intervention analysis of terrorism:the Spanish ETA case[J], Defence and Peace Economics,2003,14(6):401-402
44. Box GEP, Jenkins GM, Reinsel GC. Time series analysis:forecasting and control(3rdEd.)[M],英文影印版,人民邮电出版社,2005：462-480
45. Box G E P, Tiao G C, Intervention analysis with application to economic and environmental problems[J]. Journal of the American Statistical Association,1975, 70:70-79
46. Bruce L. Bowerman, Richard T.o'Connell. Forecasting and Time Series:An Applied Approach[M]. Beijing:Mechanical Industry Press(3rd Ed.),2003,7:657-694
47. Geoge E P, Box, Gwilym M.Jenkins. Time Series Analysis:forecasting and control [M],San Francisco,1976
48. Murtaugh P A. On rejection rates of paired intervention analysis[J]. Technical Report Series,2001 July 25
49. Murtaugh P A. Paired intervention analysis in ecology[J]. Journal of Agricultural: Biological and Environmental Statistics,2005,5(3):280-292.
50. Upali A A, Markandu A. Intervention analysis for impact assessment of irrigation systems. working paper, International Water Management Institute, P.O.Box 2075, Colombo, Sri Lanka, P27-49
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |