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.
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.
Regression analysis, Hypothesis test, Least squared methods, Generalized least squared methods, Endogeneity, Maximum likelihoods methods, Qualitative choice models
Intercultural skills | Communication skills | Specialist skills | Critical thinking skills | Practical and/or problem-solving skills |
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- | - | ✔ | - | ✔ |
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 | |
---|---|---|
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. |
Seki Asano and Jiro Nakamura (2009), "Econometrics", 2nd edition, Yuhikaku (Japanese)
Chiohiko Minotani and Atsushi Maki eds. (2010) "The handbook of applied econometrics", Asakura-shoten (Japanese)
Exam 60%, exercise problems 40%.
Students must have successfully completed both Econometrics I (IEE:B207) and Econometrics II (IEE:B301) or have equivalent knowledge.