我国城市空气污染指数的空间溢出性研究
作者：和占琼 著
出版时间：2014年版
内容简介
Urban air quality has become a big concern in China. To monitor and improve the urban air quality, Chinese government has begun to make many efforts. Inter-regional cooperation to cut and improve air quality has been required. The objective of this thesis is to study the volatil- ity of city APls of China through 42 sample cities, and to find a prime parsimonious model which gives the best forecast. Then, based on the first stage of study, this book examine time varying correlations of the city APls with the regional and national levels so as to check the im-pact of shock from region and the whole nation to the local cities; to examme whether the im-pact is mainly from the region or the nation; the existence of region homogeneity and/or heter-ogeneity. Further, study the seasonality of the 42 sample cities and their influence on the dy-namic correlations of the sample cities with the regional and national levels. Finally, the study Examine the dependence structure of city API and its corresponding regional, national level.
目录
Abstract
Chapter 1 Introduction
1.1 Statement and Significance of the Problem
1.2 Literature Review
1.2.1 Air Pollution Studies
1.2.2 Air pollution Forecasting
1.2.3 Volatility Studies
1.2.4 Regional Nature of the Air Pollution
1.2.5 Inter Region Contagion of Air Pollution
1.2.6 Seasonality of Air Pollution
1.2.7 Copula Model
1.3 Principles,Models,Rationale or Hypothesis
1.4 Objectives of Study
1.5 Scope of Study
1.6 Definitions
1.6.1 Air Pollution Index
1.6.2 Region
1.6.3 Season
1.7 0verview
Chapter 2 Methodology
2.1 Univariate Model
2.1.1 GARCH Model
2.1.2 GJR-GARCH
2.1.3 EGARCH
2.1.4 GARCH in Mean
2.1.5 Models with Dummy
2.2 Multi-variate Model
……
Chapter 3 Air Pollution Uncertainty Modelling Based on Urban API: A Case of Beljing,China
Chapter 4 Seasonality,Dynamic Correlations between Urban Air Pollution Indices and Corresponding RegionaI,National Levels in Ch:ina
Chapter 5 Modeling Dependence Dynamics of Air Pollution: Time Series Analysis Using a Copula Based GARCH Type Model
Chapter 6 Conclusion
References
