1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问,请联
系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
计量经济学 Econometrics
2.
授课院系
Originating Department
金融系 Department of Finance
3.
课程编号
Course Code
FIN303
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业核心课 Major Core Courses
6.
授课学期
Semester
秋季 Fall
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
他授课教师)
Instructor(s), Affiliation&
Contact
For team teaching, please list
all instructors
孙便霞,教学讲师,金融系
SUN Bianxia, Lecturer, Department of Finance
Email: sunbx@sustech.edu.cn
Phone:0755-88018601
办公室:慧园 3 317
Office: Wisdom Valley, 3#317
伍继松, 金融系, 13760303662
Jisong WU, Department of Finance, 13760303662
9.
/
方式
Tutor/TA(s), Contact
张璐瑶 ZHANG Luyao
Email: 11849138@mail.sustech.edu.cn
10.
选课人数限额(不填)
Maximum Enrolment
Optional
2
授课方式
Delivery Method
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
OtherPlease specify
总学时
Total
11.
学时数
Credit Hours
48
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
概率论与数理统计 Probability and Statistics MA212
微观经济学 Microeconomics FIN201
宏观经济学 Macroeconomics FIN204
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
金融实证分析方法 Empirical Methods in Finance FIN302
量化投资分析 Quantitative Investment Analysis FIN413
14.
其它要求修读本课程的学系
Cross-listing Dept.
None
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
此课程旨在讲授计量经济学的分析方法及其在经济领域中的应用,使学生在掌握计量经济学领域里基础理论内容的同时,
学会对真实经济社会中的现象进行计量建模分析。同时,该课程也会对计量经济学领域里相对高等级的一些内容和方法做
简要介绍。
This course aims to teach students the methodologies of econometrics and their applications in the realm of economy.
Besides mastering the basic theories and methods of econometrics, students are also expected to be capable of making
econometric model and analyzing a certain economic problem in the real world. In addition, this course also introduces
some high-level contents and analytical tools in the field of econometrics.
16.
预达学习成果 Learning Outcomes
在课程结束时,学生应该能够
(1) 了解计量经济学的基本分析方法;
(2) 掌握回归分析的理论知识;
(3) 对真实经济金融问题进行计量分析并解释分析结果。
By finishing this course, students should be able to
(1) Learn about the basic methodologies of econometrics;
(2) Master theoretical knowledge of regression analysis;
(3) Econometrically model the real economic problems and interpret empirical findings.
17.
课程内容及教学日历 (如授课语言以英文为主,则课程内容介绍可以用英文;如团队教学或模块教学,教学日历须注明
主讲人)
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
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1 计量经济学的性质与经济数据 2 hours
在本章节中,学习者将了解到计量经济学的研究领域,及在应用计量经济方法过程中所遇到的一般问题。
第一部分 截面数据的回归分析 22 hours
在第一部分,学习者将了解到横截面数据的回归分析。
2 简单回归模型 2 hours
在本章节中,学习者将学习解释简单回归模型,了解简单回归模型作为经验分析的一般性工具的局限性。
3 多元回归分析:估计 3 hours
在本章节中,学习者将学习多元回归模型,并进一步讨论多元回归与简单回归相比的优势。学习者还将了解在
多元回归模型中如何使用普通最小二乘法来估计模型参数,学习 OLS 估计量的各种统计性质。
4 多元回归分析:推断 3 hours
在本章节中,学习者将继续学习多元回归模型,并转向对总体回归模型中的参数进行假设检验的问题。包括对
单个参数的假设检验、对设计不止一个参数的假设检验、以及对多重限制进行假设检验。
5 多元回归分析:OLS 的渐近性 2 hours
在本章节中,学习者将了解估计量和检验统计量的渐进性质或大样本性质,以及即使没有正态性假定,t
量和 F 统计量也近似服从 t 分布和 F 分布,至少在大样本容量的情况下如此。
6 多元回归分析:其他问题 4 hours
在本章节中,学习者将学习把多元回归分析中的几个问题集中到一起,本章的内容在将多元回归应用于广泛的
实证问题时具有重要地位。
7 含有定性信息的多元回归分析:二值(或虚拟)变量 3 hours
在本章节中,学习者将了解探讨定性自变量,并学习如何在多元回归中容易地包含定性的解释变量,此外,学
习者还将学习二值因变量。
8 异方差性 3 hours
在本后果
施,及如何检验异方差性的出现。
9 模型设定和数据问题的深入探讨 2 hours
在本章节中,学习者将重点了解函数形式设误所造成的后果,以及如何对它进行检验。了解代理变量的使用如
何能解决或减轻遗漏变量的偏误。
第二部分 间序列数据的回归分析 9 hours
在第二部分中,学习者在对如何应用多元回归模型处理横截面数据问题有了清楚的了解之后,在本部分将学习
时间序列数据计量经济学。
10 时间序列数据的基本回归分析 3 hours
在本章节中,学习者将开始接触使用时间序列数据的线性回归模型,并研究用于估计这种模型的 OLS 的性质。
11 用时间序列数据计算 OLS 的其他问题 3 hours
在本章节中,学习者将掌握在用时间序列数据做回归分析时,使用通常的大样本近似所需要的一些重要概念。
学习者应认识到时间序列问题的大样本分析比之于横截面问题的大样本分析遇到的困难要多得多。
12 时间序列回归中的序列相关和异方差 3 hours
在本中,习者多元归模差项列相这一题。解在含了相关
OLS 的性质,学习如何检验序列相关,及如何对序列相关进行补救。了解使用差分过的数据是怎样常能消除
差的序列相关的。
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第三部分 深专题讨论 15 hours
在本部分中,学习者将学习一些专门的问题。
13 跨时横截面的混合:简单面板数据方法 3 hours
在本章节中,学习者将学习两种数据集,一种是独立横截面,一种是纵列数据集。
14 高等面板数据分析方法 3 hours
在本,这使
遍。虽然它们颇难以描述和实施,却得到了几个计量经济学软件包的支持。
15 工具变量估计与两阶段最小二乘法 3 hours
在本章节中,学习者将进一步学习多元回归模型中的内生解释变量问题,了解如何用工具变量法来解决一个或
多个解释变量的内生性问题。
19 一个经验项目的实施 6 hours
在本章节中,学习者将以完成一篇课程报告为重点,讨论一项成功的经验实证分析的构成要素。
CH1 The Nature of Econometrics and Economic Data 2 hours
In this chapter, students will discuss the scope of econometrics and raises general issues that arise in the
application of econometric methods.
PART 1 Regression Analysis with Cross-Sectional Data 22 hours
Part 1 of the text covers regression analysis with cross-sectional data.
CH2 The Simple Regression Model 2 hours
In this chapter, students will learn how to interpret the simple regression modeland know about that the
simple regression model has limitations as a general tool for empirical analysis.
CH3 Multiple Regression Analysis: Estimation 3 hours
In this chapter, students will learn the multiple regression models and further discuss the advantages of
multiple regressions over simple regressions. Students will also know about how to estimate the parameters
in the multiple regression models using the method of ordinary least squares, and describe various statistical
properties of the OLS estimators.
CH4 Multiple Regression Analysis: Inference 3 hours
In this chapter, students will continues learning multiple regression analysis, and turn to the problem of
testing hypotheses about the parameters in the population regression model, which includes that testing
about individual parameters, how to test a single hypothesis involving more than one parameter, and test
multiple restrictions.
CH5 Multiple Regression Analysis: OLS Asymptotics 2 hours
In this chapter, students will learn the asymptotic properties or large sample properties of estimators and test
statistics, and know that even without the normality assumption (Assumption MLR.6), t and F statistics have
approximately t and F distributions, at least in large sample sizes.
CH6 Multiple Regression Analysis: Further Issues 4 hours
In this chapter, students will learn to bring together several issues in multiple regression analysis that we
could not conveniently cover in earlier chapters, and these topics are important for applying multiple
regression to a broad range of empirical problems.
CH7 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 3
hours
In this chapter, students will learn to discuss qualitative independent variables, and know about how
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qualitative explanatory variables can be easily incorporated into multiple regression models. Students will
also learn to discuss a binary dependent variable.
CH8 Heteroskedasticity 3 hours
In this chapter, students will review the consequences of heteroskedasticity for ordinary least squares
estimation, and learn the available remedies when heteroskedasticity occurs, and also know about how to
test for its presence.
CH9 More on Specification and Data Issues 2 hours
In this chapter, students will know about the consequences of functional form misspecification and how to
test for it. Know about how the use of proxy variables can solve, or at least mitigate, omitted variables bias.
PART 2 Regression Analysis with Time Series Data 9 hours
After having a solid understanding of how to use the multiple regression model for cross-sectional
applications, students can turn to the econometric analysis of time series data.
CH10 Basic Regression Analysis with Time Series Data 3 hours
In this chapter, students will begin to study the properties of OLS for estimating linear regression models
using time series data.
CH11 Further Issues in Using OLS with Time Series Data 3 hours
In this chapter, students will learn the key concepts that are needed to apply the usual large sample
approximations in regression analysis with time series data. And students should realize that large sample
analysis for time series problems is fraught with many more difficulties than it was for cross-sectional
analysis.
CH12 Serial Correlation and Heteroskedasticity in Time Series Regressions 3 hours
In this chapter, students will discuss the critical problem of serial correlation in the error terms of a multiple
regression model. Knowing about the properties of OLS when the errors contain serial correlation, learn how
to test for serial correlation, and how to correct for serial correlation under the assumption of strictly
exogenous explanatory variables. Learning how using differenced data often eliminates serial cor relation in
the errors.
PART 3 Advanced Topics 15 hours
In this part, students will turn to some more specialized topics.
CH13 Pooling Cross Sections across Time: Simple Panel Data Methods 3 hours
In this chapter, students will learn two kinds of data sets: independently pooled cross section and panel data
set.
CH14 Advanced Panel Data Methods 3 hours
In this chapter, students will learn two methods for estimating unobserved effects panel data models that are
at least as common as first differencing. Although these methods are somewhat harder to describe and
implement, several econometrics packages support them.
CH15 Instrumental Variables Estimation and Two Stage Least Squares 3 hours
In this chapter, students will further study the problem of endogenous explanatory variables in multiple
regression models, and know about how the method of instrumental variables (IV) can be used to solve the
problem of endogeneity of one or more explanatory variables.
CH19 Carrying Out an Empirical Project 3 hours
In this chapter, students will learn the ingredients of a successful empirical analysis, with emphasis on
completing a term project.