计量经济学李峰英文版

1、Syllabus ofEconometricsCourse Name：econometrics Course Code： Credits：2.5 Total Credit Hours：48 Lecture Hours：32 Experiment Hours：16 Programming Hours： Practice Hours：Total Number of Experimental (Programming) Projects 8 , Where, Compulsory ( 8), Optional ( 0 ).School：School of Business Target Major：

2、Economics,Management、Course Nature & Aims（Times New Roman、Font Size 9）Econometrics is the core course of economics in colleges and universities.The purpose of this course is to enable students to master the basic theories and empirical research methods of modern economic research and economic analys

3、is, to propose hypotheses based on economic theories, to establish empirical economic models and to use data and computer software to solve models, test models and explain real economic problems.Therefore, in the course of teaching, econometrics theory and application model are integrated.The classi

4、cal content is the main content, and the latest model method is introduced.For the theoretical method, the emphasis is on the train of thought rather than the mathematical process;For application models, emphasis is placed on the methodology for their generation and development.This course will comb

5、ine STATA and e-views software to make the estimation and application of econometric models more intuitive, with emphasis on practice and application at the undergraduate level.At the same time, combining with the development of modern econometrics, this paper introduces related research fields in t

6、he frontiers of econometrics, providing a good foundation for students to further their study.、Course Objectives 1. Moral Education and Character Cultivation.Students should have a comprehensive understanding of econometrics related knowledge after learning econometrics theory and technology through

7、 the course.Through the explanation of the history of econometrics and the establishment of relevant theories and technologies, we can understand how the predecessors thought and overcome the obstacles in the development of econometrics, and help the students to establish scientific thinking methods

8、 and the spirit to face challenges in work.From econometric disciplines perspective, the role of development of Chinas innovation drive with outstanding contributors work as the carrier, the socialist core values education into the teaching content and teaching process each link, highlight the value

9、 guidance, knowledge and ability training and help students recognize the historical law, accurately grasp the basic national conditions and master the scientific world outlook and methodology, set up the correct world outlook and values.2.Course ObjectivesThrough the study of this course, students

10、qualities, skills, knowledge and abilities obtained are as follows：Objective 1. Good humanistic quality and professional ethics（Corresponding to Chapter 1, supporting for graduation requirements index 1.1,1.2）Objective 2. Comprehensive knowledge ability（Corresponding to Chapter 2-11, supporting for

11、graduation requirements index 2.2,2.3,2.5）Objective 3. Problem analysis ability （Corresponding to Chapter 2-11, supporting for graduation requirements index 3.3）Objective 4. Tool application capability （Corresponding to Chapter 2-11, supporting for graduation requirements index 7.1）Objective 5. Inno

12、vation and entrepreneurship （Corresponding to Chapter2-11, supporting for graduation requirements index11.1,11.2）Objective 6. Lifelong learning ability （Corresponding to Chapter2-11, supporting for graduation requirements index 12.1,12.2）3. Supporting for Graduation RequirementsThe graduation requir

13、ements supported by course objectives are mainly reflected in the graduation requirements indices x.x, x.x, x.x. , as follows:Supporting for Graduation RequirementsCourse ObjectivesGraduation RequirementsIndices and Contents Supporting for Graduation RequirementsTeaching TopicsLevel of Support Indic

14、esContentsObjective 1Good humanistic quality and professional ethicsIndex 1.2,1.2Have a noble humanistic quality, a healthy body and mindHave a high sense of social responsibility and professional ethicsChapter 1MObjective 2Comprehensive knowledge abilityIndex 2.2,2.2,2.32-2. Master the basic knowle

15、dge of management and economics, and can use it to analyze the relationship and problems between accounting and economy2-3.Master the basic knowledge of computer, and can analyze and set up models to solve empirical problems2-5.Master relevant knowledge of economic statistics,and can establish appro

16、priate analysis model for economic business, and can inference and solve the problemsn based on the modelChapter2-11HObjective 3Problem analysis abilityIndex 3.33-3. Can establish analysis model combined with relevant data, and verify hypothesis and draw valid conclusionsChapter2-11HObjective 4Tool

17、application capabilityIndex 7.17-1.Master basic computer operation and application, and be able to carry out computer program design and system management independentlyChapter2-11HObjective 5Innovation and entrepreneurshipIndex 11.1,11.211-1.Master the basic theory of technology innovation, and have

18、 the comprehensive ability to design, plan, operate and control projects11-2. Master the basic theories and methods of entrepreneurship and employment, and formulate career development plans according to their own development directionChapter2-11HObjective 6Lifelong learning abilityIndex 12.1,12.212

19、-1.Have the ability to understand and evaluate the impact of complex issues, such as accounting, finance, taxation and auditing, on environmental and social sustainability 12-2. Familiar with the industry and professional development direction, understand the research focus of the disciplineChapter2

20、-11H、Basic Course Content (Center、Times New Roman、Font Size 10、Bold)（Times New Roman、Font Size 9）Chapter 1 introduction to econometrics (supporting course objectives 1)1.1 What Is Econometrics1.2 Analysis Of Econometric Modeling ProcessTeaching Requirements: After learning this chapter, students sho

21、uld master the basic process of econometric analysis modeling: theory - hypothesis - model - data - estimation test,understand the basic logic of empirical analysis method, and understand some possible problems in the application of econometric analysis method. Students should familiar with the diff

22、erence between the non-experimental data (observational data) commonly used in econometrics and the experimental data used in natural science in the application of methods, and understand the particularity of causal inquiry in economic analysis.Basic awareness of abstract models of economic problems

23、 studied.Key Points：The Basic Process of Econometric Analysis And ModelingDifficult Points：The Particularity of Econometric Causal InquiryChapter 2 Simple Linear Regression Model(supporting course objectives 2, 3,4,5,6)2.1 Assumptions of the Classic Simple Linear Regression Model2.2 Least Squares Es

24、timation of Parameters2.3 Properties of Least Squares Estimators2.4 Coefficient Significance Test2.5 Prediction Error And Prediction IntervalTeaching Requirements: After learning this chapter, students should understand the basic idea of regression analysis, understand the assumption of classical re

25、gression analysis, and Gauss-Markov Theorem. Students should grasp the method of parameter estimation of least squares estimation, understand the nature of the parameter estimation. Students should grasp the method of parametric test, and could be establish model for practical economic problems, and

26、 give a Reasonable explanation for model and its output. students should able to solve simple linear regression problems by using computers and correctly interpret the results of computer data processing by combining statistical knowledge and economic knowledge.Key Points：The Properties of Least Squ

27、are Estimation, Parameter Estimation and Parameter TestDifficult Points：The Properties of Least Square Estimate ,Parameter Estimate , Parameter TestChapter 3. Multiple Linear Regression Model(supporting course objectives 2, 3,4,5,6)3.1 Assumptions of The Classic Multiple Linear Regression Model3.2 L

28、east Squares Estimation Of Parameters3.3 BLUE Properties Of Least Squares Estimator3.4 Dispersion Form And Determining Coefficient Of The Model3.5 Distribution Properties Of Parameter Estimation3.6 MulticollinearityComplete experimental (programming) project 1: Simple Regression Analysis and Multiva

29、riate Regression AnalysisTeaching requirements: After learning this chapter, students should master the basic assumptions of multiple regression analysis, understand the multicollinearity problem in multiple regression analysis, be familiar with the BLUE property of least squares estimator, and be a

30、ble to use computer software for parameter estimation and hypothesis test of regression analysis.Understand the economic significance of regression model parameters and the construction of parameter test statistics.Key Points： BLUE Property of Least Square Estimate, Parameter Estimation And Paramete

31、r Test, Multicollinearity.Difficult Points: BLUE Property of Least Square Estimate.Chapter 4 Heteroscedasticity (supporting course objectives 2, 3,4,5,6)4.1 Causes of Heteroscedasticity4.2 Consequences of Heteroscedasticity4.3 Test of Heteroscedasticity4.4 Solutions to Heteroscedasticity Complete ex

32、perimental (programming) project 2: HeteroscedasticityTeaching Requirements: After learning this chapter, students should understand the statistical form and economic significance that violate the basic assumption of classical regression analysis; Students should understand the consequences of heter

33、oscedasticity, master the basic test method of heteroscedasticity, model estimation method. Students should familiar with two forms of residual chart analysis tools. Students should master the G-Q test statistics, White test statistics (Glejser test) and parker (Park test) ARCH test, when there is a

34、 heteroscedastic. Students should master equation estimation methods, the method of changing model, the method of weighted least squares and the method of logarithmic transformation. Students should master generalized least squares and feasible generalized least squares . Students should understand

35、the robust estimate in the presence of heteroscedasticity, skilled use of computer to solve the possible when heteroscedasticity to be exist.Key Points：Heteroscedasticity Test, Corrected Estimate and Robust EstimateDifficult Points：Corrected Estimate and Robust EstimateChapter 5 Autocorrelation (sup

36、porting course objectives 2, 3,4,5,6)5.1 Reasons For Autocorrelation5.2 Consequences Of Autocorrelation5.3 Test of Autocorrelation5.4 Autocorrelation SolutionsComplete experimental (programming) project 3: AutocorrelationTeaching Requirements: After learning this chapter, students should understand

37、the statistical form and economic significance that violate the basic assumptions of classical regression analysis; Students should understand the consequences of autocorrelation, master the basic test method and model estimation method of autocorrelation. Students should master two kinds of residua

38、l diagram analysis tools, master D-W test, Regression test, High-Order Autocorrelation test: partial autocorrelation test, Q test, and Lagrange multiplier test. Students should master the estimation methods of with heteroscedasticity: generalized difference method, Dubin two-step method, Cochran-Orc

39、utt iterative method.students should use of computer skillfully to solve the problem of model estimation when heteroscedasticity may be exist.Key Points：D-W test, Partial Autocorrelation Test and Q TestDifficult Points：Generalized Difference Method, Dubin Two-Step Method, Cochran-Orcutt Iterative Me

40、thodChapter 6 Multicollinearity (supporting course objectives 2, 3,4,5,6)6.1 Causes of Multicollinearity6.2 Influence of Approximate Multicollinearity On Model Estimation6.3 Test of Multicollinearity6.4 Multicollinearity SolutionsTeaching Requirements: After learning this chapter, students should un

41、derstand the concept of multicollinearity, understand the reasons of the economic problems of multicollinearity, understand the multicollinearity of model to estimate the influence of the difference, familiar with master the discriminant method of multicollinearity, mastering discriminant method, co

42、rrelation coefficient test, Klein Farrar, Glauber (Farrar, Glauber) test, familiar with variance inflation factor inspection. Students should master the estimation methods of the existing multicollinearity model: stepwise regression method, principal component regression method, the combination of t

43、ime series and interface data. Students should master the estimation of multicollinearity model is solved by computer software.Key Points：Test of Multicollinearity and Estimation When Its ExistDifficult Points：Test of Multicollinearity and Estimation When Its ExistChapter 7 Dummy Variables and Rando

44、m Explanatory Variables (supporting course objectives 2, 3,4,5,6)7.1 Dummy Variable Concepts and Applied Rules7.2 Application of Dummy Variables in Stability Test of Model Structure7.3 Application of Dummy Variables in Piecewise Regression7.4 Application of Dummy Variables in Mixed Regression7.5 App

45、lication of Dummy Variables in Outlier Problems7.6 Dummy Explained Variables7.7 Random Explanatory Variables Complete experimental (programming) project 4(Optional): Dummy VariablesTeaching Requirements: After learning this chapter, students should master the addition rules and multiplication rules

46、when you introduce dummy variables into the model. Students should understand the meaning of estimate of coefficient of dummy variables in addition and multiplication methods. Students should familiar with introducing dummy variables to test the stability of economic structure. Students should maste

47、r the estimation and test of three models of the dummy explained variables: linear probability model, logit model and probit model, and explain the economic significance of the computer output results of the three models. Students should understand the problem of random explanatory variables and the

48、ir consequences, and master the method of instrumental variables.Key Points：Stability of Economic Structure, Linear Probability Model, Logit Model, Probit Model, Instrumental Variable Method.Difficult Points：Endogeneity And Instrumental Variable MethodChapter 8 lagged variable model (supporting cour

49、se objectives 2, 3,4,5,6)8.1 Overview Of The Lagged Variable Model8.2 Finite Distributed Lag Model And Its Estimation8.3 Geometrically Distributed Hysteresis Model8.4 Estimae of Autoregressive ModelComplete experimental (programming) project 5: lagged variable modelTeaching Requirements: After learn

50、ing this chapter, students should understand the lag phenomenon and its causes in the economic model, understand the economic meaning of the distributed lag model and the autoregressive model, and understand the difficulties in the estimation of the distributed lag model. Students should master the

51、estimation methods of distributed lag model: empirical weighting method, Almon Iteration Method, and Koyck Iteration Method. Students should understand two geometric distributed lag models based on economic theory: adaptive expectation model and local adjustment model. Students should master the est

52、imation method of autoregressive model, Implement Instrumental variable method, and Generalized Difference Method by computer. Key Points：Almon Iteration Method, Koyck Iteration Method, Adaptive Expectation Model, Partial Adjustment Model Difficult Points：Estimation Method Of Regression Model Of Lag

53、 VariablesChapter 9 Time Series Data (supporting course objectives 2, 3,4,5,6)9.1Properties of Time Series Data9.2 Stationarity of Time Series9.3 Co-Integration and Error Correction Model9.4 Granger Causality Test9.5 Vector Autoregressive Model Complete experimental (programming) project 6: Time Ser

54、ies DataTeaching Requirements: After learning this chapter, students should understand the basic properties of time series data, understand the concept of stationarity of time series data, understand the concept of unit root, and master the basic methods of stationarity test: DF test, ADF test, Phil

55、lips-Perron test. Students should understand the concepts of data integration and co-integration, understand engle-granger test, understand the error correction model and the establishment and estimation of the error correction model, and master the Engle-Granger two-step method. Students should est

56、imate time series model using computers skillfully in business.Key Points：Unit Root, DF test, ADF test, Error Correction Model（ECM）Difficult Points：Stationarity and Unit RootsChapter 10 Simultaneous Equation Model (supporting course objectives 2, 3,4,5,6)10.1 Basic Concepts of Simultaneous Equation

57、Model10.2 Identification of Simultaneous Equation Model10.3 Estimation of Simultaneous Equation Model10.4 Test of Simultaneous Equation Model Complete experimental (programming) project 7: Simultaneous Equation ModelTeaching Requirements: After learning this chapter, students should understand the c

58、oncepts of simultaneous equations. Students should understand the concepts of endogenous variables, exogenous variables, and predeterminate variables. Students should understand the simplification and structural formula of simultaneous equations. Students should understand the difficulty of simultan

59、eous equation identification, master the criteria of simultaneous equation identification: order condition and rank condition. Students should master several estimation methods of simultaneous equations, including recursive model estimation method, instrumental variable method, indirect least square

60、 method, two-stage least square method. Students should be able to set up models in business analysis and use computers to solve simultaneous equation problems.Key Points：Recognition of Simultaneous Equation, Indirect Least Square Method, Two - Stage Least Square Method.Difficult Points：Recognition