| 1. | Model building and identification system of chaotic economic time series
混沌经济时间序列的建模与系统识别 |
| 2. | Wavelet network - based non - linear economic time series prediction model
基于小波网络的非线性经济时序预测模型 |
| 3. | " for methods of analyzing economic time series with time - varying volatility arch
所发明的“自动递减条件下的异方差性” |
| 4. | " for methods of analyzing economic time series with common trends cointegration
.以表彰他们在“分析经济时间数列”研究领域所作出的突出贡献 |
| 5. | The autoregressive conditional heteroskedastic ( arch ) class of models for conditional variances was put forward by engle ( 1982 ) proved to be extremely useful for analyzing economic time series . garch models have been developed to account for empirical regularities in financial data
Engle ( 1982 )提出的arch模型，对经济时间序列中的条件方差分析十分有用， arch模型可以很好地刻划金融数据。 |
| 6. | Second , introduce the measuring of business cycle in economic time series . third , we sum up the basic method of dividing business cycle phase , and research the mode of expansion and recession around economic fluctuation in china
三、对基本的经济周期性波动的阶段划分方法进行了归纳,以此方法为基础采用各个变量相对于gdp趋势的数值序列,研究了我国经济波动的扩张和收缩模式,并以政策回归的方法检验了经济政策的效应。 |
| 7. | Professor sir clive granger is a pioneer in the field of time series analysis and econometrics . he received the 2003 nobel prize in economics for his contributions to methods of analyzing long run relationships in economic time series , a discovery which was a major breakthrough . his models have become indispensable tools for macro - economic forecasts , evaluation of risks and analysis of the financial markets
格兰杰教授是计量经济学及时间序列分析的大师，他以研究经济数据之间的长远关系即:协整cointegration模型获2003年诺贝尔经济奖，为经济学上一重大突破，他发明的分析模型被广泛应用于宏观经济预测分析风险评估及金融市场的分析。 |
| 8. | Time series method is a newly - developed quantitative method for prediction and yields satisfactory results in the analysis of economic time series in which the involved factors are too many and the relationships between them are too complicated , leading to the application of theory - based quantitative predicting methods unworkable
时间序列分析方法是最近发展起来的定量预测方法，它特别适用于经济时间序列，因为经济现象涉及因素较多，关系又比较复杂，因此难以用量化的唯理模型进行预测分析。 |
