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This course is aimed to introduce the basic knowledge of Statistics and Econometrics as a tool for economic analysis.
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1.What is Statistics?
Key Concepts & Terminology in Statistics
Descriptive vs. Inferential Statistics
Sample vs. Population, Parameter vs. Statistic (Estimator)
Birds Eye View of Logic & Purpose of Statistical Inference
2.,3. Descriptive Statistics
Expected Value and Variance: Definition and Property
Measure of Central Tendency - Mean, Median, Mode
Measure of Dispersion - Variance, Standard Deviation, Mean Deviation
Other Measures - Skewness, Kurtosis, Moments
Change of Variable and Standardization, Covariance & Correlation
4. Normal Distribution
Definition and Key Property, Central Limit Theorem, Excel Experiment
5. Interval Estimation
Random Sampling, Central Limit Theorem, Sampling Distribution
Confidence Interval
Interval Estimation of Mean: Known Variance, Unknown Variance -T-Test
6. Hypothesis Testing
Formal Procedure of Hypothesis Testing
Null Hypothesis vs. Alternative Hypothesis
Accepting / Rejecting Hypothesis, Significance Level, p-Value
One-sided or Two Sided Test?, Two Types of Error, Power (of Test)
7. Mid-Term Quiz + Review of Statistical Concept
8. Regression Analysis
Fitting a line & Goodness of the Fit
Explanatory (Independent) Variable, Explained (Dependent) Variable
Fitting a line via OLS, Normal Equations, Coefficient of Determination
9. Statistical Analysis of Regression Outcome
OLS estimators as random variable, Mean & Variance of OLS estimators
Hypothesis Test of OLS estimators via t Test
10. Re-examination of Classical Assumptions on Error Term
Zero Mean, Constant Variance, Independence, No Serial Correlation, Normality, BLUE and BUE
11. Functional Form
Constant Term,
Functional Form (Linear, Log-Log, Lin-Log, Log-Lin, Inverse, Polynomial)
Lagged Independent Variable, Dummy Variable (Intercept & Slope Dummy)
12. Choice of Independent Variables
Omitted Variable & Irrelevant Variables
Specification Criteria (i) - Theory, t-Test, , Bias
Specification Criteria (ii) - Ramsey’s Test, AIC, Schwartz Criterion
13. Serial Correlation
Nature &Consequences
Diagnosis - Lagged Residual Graph, Durbin-Watson Statistic
Remedies - Generalized Least Square (AR(1), Cochrane-Orcutt)
Newey-West Standard Errors
14. Heteroskedasticity
Nature &Consequences
Diagnosis - Redefining Variables, White Test, Park Test
Remedies - White HC Standard Error, Redefining Variable
15. Multicollinearlity
Nature &Consequences
Diagnosis - Correlation Coefficient, Variance Inflation Factor
Remedies - Do Nothing, Increasing Data, Redefining Variable
16. Final Test
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I will inform before the class
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1. To be able to perform the interval estimation and hypothesis testing
2. To be able to perform the basic regression analysis and be able to take necessary remedy measures when the classical assumptions are not met.
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1. To be able to perform the interval estimation and hypothesis testing
2. To be able to perform the basic regression analysis and be able to take necessary remedy measures when the classical assumptions are not met.
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Homework & Quiz (20%), Mid-Term (20%), Final Exam (60%)± Adjustment based on the overall performance (Class Participation, Enthusiasm, etc...)
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【成績評価の基準表】
| 秀(S) | 優(A) | 良(B) | 可(C) | 不可(F) |
| 履修目標を越えたレベルを達成している | 履修目標を達成している | 履修目標と到達目標の間にあるレベルを達成している | 到達目標を達成している | 到達目標を達成できていない |
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履修目標：授業で扱う内容（授業のねらい）を示す目標
到達目標：授業において最低限学生が身につける内容を示す目標
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One Lecture per week with a quiz at the end plus weekly homework
Students will have a chance to perform actual statistical calculations & analysis using Excel and Eviews during the lecture.
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Studenmud's textbook supplemented by the lecture will be something like a Gujarati's book.
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Statistics, Interval Estimation, Hypothesis Testing, Regression Analysis, Least Square, T-Test, P-value,
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