Econometrics ｜シラバス情報

授業科目名

Econometrics

(英語名)

Econometrics

科目区分

専門教育科目

-

対象学生

国際商経学部

学年

2年

ﾅﾝﾊﾞﾘﾝｸﾞｺｰﾄﾞ

KCCBG2MCA7

単位数

2単位

ナンバリングコードは授業科目を管理する部局、学科、教養専門の別を表します。詳細は右上の？から別途マニュアルをダウンロードしてご確認ください。

授業の形態

講義・演習 (Lecture/Seminar)

開講時期

2024年度前期

(Spring semester)

担当教員

高橋新吾、高橋新吾

所属

Global Business Course

授業での使用言語

英語

関連するＳＤＧｓ目標

目標5／目標9／目標10

ｵﾌｨｽｱﾜｰ・場所

Before or after class, at the classroom.

連絡先

takahashi@em.u-hyogo.ac.jp

対応するディプロマ・ポリシー(DP)・教職課程の学修目標

二重丸は最も関連するDP番号を、丸は関連するDPを示します。

学部DP

1◎／2◎

研究科DP

ー

全学DP

ー

教職課程の学修目標

目標１:磨き続ける力

講義目的・到達目標

【Lecture objective講義目的】
Econometrics is used to test economic hypothesis using data. For example, the simplest economic theory predicts that an increase in minimum wage would increase unemployment, but this theory must be tested using the actual data. Econometrics provides you with the most powerful tool to do so. Econometrics is valuable not only for your graduate thesis research but also for the actual decision making in the real business and other setting.

【Target到達目標】
We aim to provide students with the basic understanding of econometric theories and tools, and to equip students with necessary skills in carrying out regression analyses for empirical research and then to interpret the results in a scientific way.

授業のサブタイトル・キーワード

サブタイトル： Econometrics
キーワード：Ordinary least square, statistical tests, causality, policy analyses

講義内容・授業計画

【講義内容】
For each topic, I will provide the theoretical treatment of the method first, and then I will provide a real life data, and we will estimate models together, and discuss the results.
【授業計画】
Topic 1. The mechanism of a regression. Causality and correlation (Lecture 1)
Topic 2. Conditions for the causality (Lecture 2)
Topic 3. Simple linear regressions, standard errors of the estimated coefficients (Lecture 3~4)
Topic 4. Multiple linear regressions (Lecture 5) I will provide real dataset and we will estimate the models together.
Topic 5. Omitted variable bias (Lecture 6)
Topic 6. Hypothesis testing (T-test, F-test, P-values) (Lecture 7~8)
Topic 7. Setting an estimation model, and making it flexible (Lecture 9) I will provide real dataset and we will estimate the models together.
Topic 8. Dummy explanatory variables (Lecture 10~11) I will provide real dataset and we will estimate the models together.
Topic 9. Analyses using pooled cross section data: Differences in Difference estimation (Lecture 12~13) We will estimate the effects of minimum wage on unemployment using real dataset during the class.
Topic 10. Analyses using panel data: Fixed effect estimation (Lecture 14~15) I will provide real dataset and we will estimate the models together.

教科書

I will provide lecture notes for each topic

参考文献

Introductory Econometrics: A Modern Approach, by Jeffery M. Wooldridge

事前・事後学習（予習・復習）の内容・時間の目安

【予習】You will receive the lecture notes in advance. Please go over it before the class.
【復習】All the lecture notes contain exercise questions. Please go over them after the class.

アクティブ・ラーニングの内容

Almost for all the classes, we will estimate econometric models using real data. We will interpret the results in a class discussion.

成績評価の基準・方法

【成績評価の基準】
Mastery of the concept,
Class engagement

【成績評価の方法】
Quizzes or homework 20%
Final exam 80%

課題・試験結果の開示方法

We will go over the problem sets during the class when it is appropriate to do so.

履修上の注意・履修要件

The pre-requisite for this course is the Introductory Statistics.

実践的教育

N/A

備考

N/A

英語版と日本語版との間に内容の相違が生じた場合は、日本語版を優先するものとします。