1. EVIEWS基础 ······························································································································· 3 1.1. 1.2. 1.3. 1.4. 1.5.
EVIEWS简介 ················································································································ 3 EVIEWS的启动、主界面和退出 ·················································································· 3 EVIEWS的操作方式 ····································································································· 5 EVIEWS应用入门 ········································································································· 6 EVIEWS常用的数据操作 ··························································································· 15
2. 一元线性回归模型 ······················································································································ 24 2.1. 2.2. 2.3.
用普通最小二乘估计法建立一元线性回归模型······················································· 24 模型的预测 ················································································································ 30 结构稳定性的CHOW检验 ························································································· 34
3. 多元线性回归 ····························································································································· 39 3.1. 3.2.
用OLS建立多元线性回归模型 ················································································ 39 函数形式误设的RESET检验 ··················································································· 45
4. 非线性回归 ································································································································· 48 4.1. 4.2. 4.3. 4.4. 4.5. 4.6.
用直接代换法对含有幂函数的非线性模型的估计 ··················································· 48 用间接代换法对含有对数函数的非线性模型的估计 ··············································· 50 用间接代换法对CD函数的非线性模型的估计 ······················································· 53 NLS对可线性化的非线性模型的估计 ····································································· 55 NLS对不可线性化的非线性模型的估计 ································································· 58 二元选择模型 ············································································································ 62
5. 异方差 ········································································································································· 68 5.1. 5.2. 5.3.
异方差的戈得菲尔德——匡特检验 ·········································································· 68 异方差的WHITE检验 ······························································································ 72 异方差的处理 ············································································································ 75
6. 自相关 ········································································································································· 79 6.1. 6.2.
自相关的判别 ············································································································ 79 自相关的修正 ············································································································ 83
7. 多重共线性 ································································································································· 87 7.1. 7.2.
多重共线性的检验 ···································································································· 87 多重共线性的处理 ···································································································· 92
8. 虚拟变量 ····································································································································· 94 8.1. 8.2. 8.3.
虚拟自变量的应用 ···································································································· 94 虚拟变量的交互作用 ································································································ 99 二值因变量：线性概率模型 ··················································································· 101
9. 滞后变量模型 ··························································································································· 105 9.1. 9.2.
自回归分布滞后模型的估计 ··················································································· 105 多项式分布滞后模型的参数估计 ··········································································· 110
10. 联立方程模型 ························································································································· 115 10.1. 联立方程模型的单方程估计方法 ··········································································· 115 10.2. 联立方程模型的系统估计方法 ··············································································· 119
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1. Eviews基础
1.1. Eviews简介
Eviews：Econometric Views（经济计量视图），是美国QMS公司（Quantitative Micro Software Co.，网址为http://www.eviews.com）开发的运行于Windows环境下的经济计量分析软件。Eviews是应用较为广泛的经济计量分析软件——MicroTSP的Windows版本，它引入了全新的面向对象概念，通过操作对象实现各种计量分析功能。
Eviews软件功能很强，能够处理以时间序列为主的多种类型数据，进行包括描述统计、回归分析、传统时间序列分析等基本数据分析以及建立条件异方差、向量自回归等复杂的计量经济模型。
1.2. Eviews的启动、主界面和退出 1.2.1. Eviews的启动
单击Windows的【开始】按钮，选择【程序】选项中的【Eviews 5】，单击其中的【Eviews5】；或者在相应目录下用鼠标双击所示：
标题栏 菜单栏 命令窗口 启动Eviews 5程序，进入主窗口。如图1.1
工作区 状态栏
图 1.1
3
Eviews教程-超详细完整版
