### 2016　Applied Econometrics

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Undergraduate major in Industrial Engineering and Economics
Instructor(s)
Shimane Tetsuya  Higuchi Yoichiro
Course component(s)
Lecture / Exercise
Mode of instruction

Day/Period(Room No.)
Tue5-6(W932)  Fri5-6(W932)
Group
-
Course number
IEE.B336
Credits
2
2016
Offered quarter
2Q
Syllabus updated
2016/4/27
Lecture notes updated
2016/8/8
Language used
Japanese
Access Index ### Course description and aims

In this course, We lecture to the students, who learned the econometric theory in econometrics I and II classes, about application of econometric methods for empirical data. Our goal is that the students obtain the ability for the empirical studies.

In this class, we explain some econometric methods and demonstrate the procedure of the analysis using a statistical analysis application software.
Then, as exercise at home, the students run the analysis using the same data.

### Student learning outcomes

By the end of this course, students will:
* be able to understand the empirical studies applied econometric methods and to interpret their results.
* be able to build proper models for the subjects of analysis.
* be able to evaluate the validity of the results of empirical analysis.
* be able to make intuitive implications from empirical results.

### Keywords

Regression analysis, Hypothesis test, Least squared methods, Generalized least squared methods, Endogeneity, Maximum likelihoods methods, Qualitative choice models

### Competencies that will be developed

 ✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

### Class flow

In this class, we explain some econometric methods and demonstrate the procedure of the analysis using a statistical analysis application software.
Then, as exercise at home, the students run the analysis using the same data.

### Course schedule/Required learning

Course schedule Required learning
Class 1 Guidance, The forms of Data Sets Explain the difference of data forms between cross-section, time-series, panel.
Class 2 Linear Regressions: OLS(1) Lest Squared Method, Hypothesis Test (Coefficients) Understand the notion of Ordinary least squared method. Interpret the results of t-test for coefficients.
Class 3 OLS(2) Hypothesis Test (Joint Hypothesis), Model Specification (Overspecification and underspecification) Interpret the results of F-test for joint hypothesis. Explain the problem of overspecification and underspecification.
Class 4 Linear Regressions: OLS(3) Predictions, Function Forms, Multicollinearity Explain the confidence interval of predictions. Understand the problems of multicollinearity and the appropriate action to them.
Class 5 Linear Regressions: OLS(4) Panel Data Model(FE, FD), DID Required Learning: Understand the difference between RE model and FE model, and interpret their estimates then. Understand the notion of DID.
Class 6 Linear Regressions: GLS(1) Heteroscedasticity Understand the problems of heteroscedasticity and the appropriate action to them.
Class 7 Linear Regressions: GLS(2) Serial correlation Understand the problems of serial correlation among error terms, and the appropriate action to them.
Class 8 Linear Regressions: GLS(3) SUR model, Panel Data Model(RE) Understand the SUR model and the RE panel data model.
Class 9 Endogeneity(1) Error-in-Variable model, Simultaneous Equations Model, Instrumental Variable Methods Understand the endogeneity in the error-in-variable model and in the simultaneous equations model. Understand the appropriate action to the endogeneity by the instrumental variable estimation.
Class 10 Linear Regressions: Endogeneity(2) Identification Problem Understand the notion of the identification problem and the appropriate solution to them.
Class 11 Linear Regressions: Endogeneity(3) GMM Understand the notion of GMM.
Class 12 Non-linear Regressions: Maximum Likelihoods Method, Binary Models Understand the notion of the maximum likelihoods method. Understand the notion of the binary models, and interpret their estimates.
Class 13 Non-linear Regressions: Multinomial Models, Ordered Models Understand the notion of the multinomial models and the ordered models, and interpret their estimates.
Class 14 Non-linear Regressions: Truncated/Censored Dependents, Tobit Model Understand the problems of truncated/censored dependents. Interpret the estimates of Tobit modes.
Class 15 Non-linear: Selectivity Bias, Heckit Model Understand the problems of the selectivity bias. Interpret the estimates of Heckit model.

### Textbook(s)

Seki Asano and Jiro Nakamura (2009), "Econometrics", 2nd edition, Yuhikaku (Japanese)

### Reference books, course materials, etc.

Chiohiko Minotani and Atsushi Maki eds. (2010) "The handbook of applied econometrics", Asakura-shoten (Japanese)

### Assessment criteria and methods

Exam 60%, exercise problems 40%.

### Related courses

• IEE.B207 ： Econometrics I
• IEE.B301 ： Econometrics II

### Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students must have successfully completed both Econometrics I (IEE:B207) and Econometrics II (IEE:B301) or have equivalent knowledge. 