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 | CRM(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 | CRM(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 | CRM(3) Predictions, Function Forms, Multicollinearity | Explain the confidence interval of predictions. Understand the problems of multicollinearity and the appropriate action to them. |
Class 5 | CRM(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 | Review of Classical Regression Model (CRM) | Review what was taught during classes 1-5 and conform your understanding through discussion. |
Class 7 | GCRM(1) Heteroscedasticity | Understand the problems of heteroscedasticity and the appropriate action to them. |
Class 8 | GCRM(2) Panel Data Model(RE) | Understand the RE panel data model. Understand the procedure of model specification. |
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 | Endogeneity(2) Identification Problem | Understand the notion of the identification problem and the appropriate solution to them. |
Class 11 | Review of Generalized Classical Regression Model (GCRM) and Endogeneity | Review what was taught during classes 7-10 and conform your understanding through discussion. |
Class 12 | Non-linear Regressions(1) 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(2) Multinomial Models, Ordered Models | Understand the notion of the multinomial models and the ordered models, and interpret their estimates. |
Class 14 | Non-linear Regressions(3) Truncated/Censored Dependents, Tobit Model | Understand the problems of truncated/censored dependents. Interpret the estimates of Tobit modes. |
Class 15 | Non-linear Regressions(4) 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)
Materials used in class can be found on OCW-i.
Tanaka, Ryuichi. 2015. First Steps in Econometrics, Tokyo: Yuhikaku (Japanese).
Wooldridge, Jeffrey M. 2012. Introductory econometrics: a modern approach. Mason, OH: South-Western Centavo Learning.
Minotani, Chikio and Atsushi Maki eds. 2010. The handbook of applied econometrics, Tokyo: Asakura-shoten (Japanese).
Angrist, Joshua David, and Jörn-Steffen Pischke. 2009. Mostly harmless econometrics: an empiricist's companion. Princeton: Princeton University Press.
Exam 50%, exercise problems 50%.
Students must have successfully completed both Econometrics I (IEE:B207) and Econometrics II (IEE:B301) or have equivalent knowledge.