Econometrics
Course No.:0140801 Credit(s): 3
Course Description
Econometrics is a fundamental course as well as one of the core courses for statistics major. This course is a methodology disciplines for integration economics, mathematics, statistics and computer application. From the application point of view, the econometric approach is to establish an econometric model, which is a mathematical simulation of the real economic environment.
By studying Econometrics, students can realize the position of this course in economics and grasp the corresponding basic theories and methods. Meanwhile, they can also have the ability to analyze and solve the real economic problems by using econometric approach.
Course Learning Outcomes
The student learning outcomes are what student would be able to know and to do on the completion of this course. In details are:
1. Understand the basic principles of economics modeling.
2. Know the fundamental content and work process in econometric analysis.
3. Have the preliminary ability to quantitatively analyze the economic problems.
Relationship to Other Courses
The prerequisites for this course are Mathematical Analysis, Higher Algebra, Western Economics, Probability Theory and Mathematical Statistics.
Textbook and Reading Lists
Textbook:
Hao Pang, Econometrics (3rd edition). Science Press, 2014.
Suggested reading lists:
Damodar N.Gujarati, Dawn C.Porter, Essentials of Econometrics(4thedition). China Machine Press, 2010.
Damodar N.Gujarati, Dawn C.Porter, Basic Econometrics(5thedition). China Renmin University Press, 2010.
Jeffrey M.Wooldridge, Introductory Econometrics: A Modern Approach(5thedition). Tsinghua University Press 2014.
Course Assessment
Activities | Weighting (%) |
Daily Performance and Homework | 20% |
Midterm Exam | 0% |
Final Exam | 80% |
Course Schedule
Week | Topics | Text |
1 | Lecture 1 Introduction 1.1 Definition of Econometrics 1.2 Feature of Econometrics 1.3 Objective of Econometrics 1.4 Content and Research Methods of Econometrics | Chapter 1 |
2-3 | Lecture 2 One-variable Linear Regression Model 2.1 Establishment of Model and Assumptions 2.2 Parameter Estimation of One-variable Linear Regression Model 2.3 Statistical Properties of the Least Squares Estimator 2.4 Goodness of Fit Test for Regression Equation 2.5 Significance Testing and Confidence Interval of Estimate of Regression Coefficient 2.6 Prediction by One-variable Linear Regression Model 2.7 Brief Summary 2.8 Case Analysis | Chapter 2 |
4-5 | Lecture 3 Multiple Linear Regression Model 3.1 Establishment of Model and Assumptions 3.2 Least Square Method 3.3 Properties of the Least Squares Estimator 3.4 Coefficient of Determination 3.5 Significance Testing and Confidence Interval 3.6 Other Testing for Regression Coefficient 3.7 Prediction 3.8 Case Analysis | Chapter 3 |
6-7 | Lecture 4 Linearization of Nonlinear Regression Model 4.1 Nonlinear Relation between Variables 4.2 Linearization Technique 4.3 Case Analysis | Chapter 4 |
8 | Lecture 5 Heteroscedasticity 5.1 Conception of Heteroscedasticity 5.2 Source and Consequence of Heteroscedasticity 5.3 Test for Heteroscedasticity 5.4 Correction to Heteroscedasticity——Weighted Least Square Method 5.5 Case Analysis 5.6 Brief Summary | Chapter 5 |
9-10 | Lecture 6 Autocorrelation 6.1 Non- autocorrelative Assumptions 6.2 Source and Sequence of Autocorrelation 6.3 Test for Autocorrelation 6.4 Solution of Autocorrelation 6.5 Matrix Description of Overcoming Autocorrelation 6.6 Estimate of Autocorrelation Coefficient 6.7 Case Analysis | Chapter 6 |
11 | Lecture 7 Multicollinearity 7.1 Conception of Multicollinearity 7.2 Source and Sequence of Multicollinearity 7.3 Test for Multicollinearity 7.4 Correction to Multicollinearity 7.5 Case Analysis | Chapter 7 |
12-13 | Lecture 8 Special Explanatory Variable in Model 8.1 Stochastic Explanatory Variable 8.2 Lagged Variable 8.3 Dummy Variable 8.4 Time Variable | Chapter 8 |
14 | Lecture 9 Diagnosis and Inspection of the Model 9.1 F-test for Overall Significance 9.2 t-test for a Single Regression Parameter Significance 9.3 F-test for Linear Restrictive Condition 9.4 LR-test, Wald-test and LM-test 9.5 Chow Breakpoint Tests 9.6 Jarque-Bera Normal Distribution Test | Chapter 9 |
15-17 | Lecture 10 Simultaneous Equation Model 10.1 Conception of Simultaneous Equation Model 10.2 Classification of Simultaneous Equation Model 10.3 Recognition of Simultaneous Equation Model 10.4 Recognition Criteria for Simultaneous Equation Model 10.5 Estimates for Simultaneous Equation Model 10.6 Case Analysis | Chapter 10 |
18 | Lecture 11 Review | Chapters 1-10 |
19 | Final Exam | |
