Introduction | Notes | R Book 1 | R Book 2 | R Intro | Math | ||||
L1: Regression | Notes | Codes | House Data | ||||||
L2: Tree Models | Notes | Codes | Myrpart | CV1 | CV2 | ||||
L3: Random Forest | Notes | Codes | Bootstrap | CV3 | |||||
L4: Classification | Notes | Codes | Bank Data | CV4 | YouTube | Practice Exam1 | Key | ||
L5: ARMA Models | Notes | Codes | Example | Codes | Paper Data | Hotel Data | Markov Chain | Codes | GoldenRatio |
L6: Bayesian Forecast | Notes | Codes | ARMA | Codes | Notes II | Notes III | Codes | MI | MCMC |
L7: ARCH Models | Notes | Codes | Engle Paper | Codes | Engle Data | Practice Exam2 | |||
Optional Readings | BVT | GD | LASSO | DE | LO | UR | PC | KF | |
Smoothing Moethod | Notes | Codes | |||||||
Spectral Analysis | Notes | Codes | |||||||
Sinusoidal Models | Notes | Codes | |||||||
PCA | Notes | Codes | |||||||
HAC Estimator | Notes | Codes | |||||||
MCMC---1 | Notes | Codes | |||||||
MCMC---2 | Notes | Codes 1 | Codes 2 | Codes 3 |
Introduction to R---1 | Notes | R Course | R Book 1 | R Book 2 | R Book 3 | Fun |
Introduction to R---2 | Notes | Codes | ||||
Linear Programming | Notes | Codes | Video | |||
Numerical Method | Notes | Codes | Video | |||
Utility Maximization | Notes | Codes | ||||
OLS | Notes | Codes | House Data | Book 4 | Practice Exam | Bayes code |
Quadratic Programming | Notes | Codes | Video | |||
Markowitz Portfolio Theory | Notes | Codes | Video | |||
Regression Tree | Notes | Codes | Salary Data | Video | ||
Random Forest | Notes | Codes | Video | |||
Classification | Notes | Codes | Bank Data | Video 1 | Video 2 | |
Duopoly | Notes | Codes | Practice Exam | Video 1 | Video 2 | |
Term Paper | ||||||
Introduction to Bayesian Statistics | Notes | Codes | ||||
Markov Chain | Notes | |||||
MCMC---1 | Notes | Codes | ||||
MCMC---2 | Notes | Codes 1 | Codes 2 | Codes 3 | ||
Principal Component | Notes |
Lecture 1: Introduction to Business Report | Slide | Assigment 1 | Reading A | Reading B | Reading C |
Lecture 2: Markowitz Portfolio Theory I | Slide | Assigment 2 | R Code | ||
Lecture 3: Markowitz Portfolio Theory II | Slide | Assigment 3 | R Code | ||
Lecture 4: Markowitz Portfolio Theory III | Slide | Assigment 4 | R Code | Video 1 | |
Lecture 5: CAPM I | Slide | Assigment 5 | Video 2 | Video 3 | |
Lecture 6: CAPM II | Slide | Assigment 6 | R Code | ||
Lecture 7: Factor Model | Slide | Assigment 7 | |||
Lecture 8: Efficient Market Hypothesis | Slide | Assigment 8 | |||
Business Report | Instruction |
Lecture 1: Introduction to Business Report | Slide | Assigment 1 | Reading A | Reading B | Reading C | ||
Lecture 2: Categorical Variable | Slide | Assigment 2 | Macro Data | ||||
Lecture 3: Quantitative Variable | Slide | Assigment 3 | |||||
Lecture 4: Regression Analysis | Slide | Assigment 4 | Dr. McKee1 | Dr. McKee2 | |||
Lecture 5: ARIMA Model | Slide | Assigment 5 | |||||
Lecture 6: Decomposition Methods | Slide | Assigment 6 | shampoo Data | housing Data | airline Data | X12a | X12b |
Lecture 7: Smoothing Methods | Slide | Assigment 7 | ship Data | inventory Data | |||
Lecture 8: ARIMA Forecasting | Slide | Assigment 8 | milk Data | PeerComments | Forecasting ARIMA(1,1,1) | R Code | |
Group Project | Instruction | Tips |
Introduction | Notes | R Book 1 | R Book 2 | R Intro |
Regression | Notes | Codes | House Data | |
Tree Model | Notes | Codes | ||
Random Forest | Notes | Codes | ||
Classification | Notes | Codes | Bank Data | |
Smoothing Moethod | Notes | Codes | ||
ARMA Model | Notes | Codes |
Lecture 1 | Slides | Codes | House Data | IT Resources | Video 1 | Video 2 | Video 3 | Video 4 | NCAA paper | |||
Lecture 2 | Slides | Codes | HW 1 | Our Data | Notation and Intuition | Math Note 1 | Math Note 2 | Video1 | Video2 | Video3 | Wage Data | Exercise |
Old Exams | Exam 1 | Key | Exam 2 | Key | Exam 3 (ignore Q1-Q5) | Key | ||||||
Lecture 3 | Slides | Empirical Example | HW 2 | House Data | Wage Data | Monte Carlo 1 | Monte Carlo 2 | Randomization | Experiment | |||
Lecture 4 | Slides | Empirical Example | Stata Commands | Stata Results | ANOVA Jellyfish | |||||||
Lecture 5 | Slides | Empirical Example | Codes | HW 3 | Empirical Example II | Codes | Wage Data | NBA Data | Video 1 | Video 2 | ||
Lecture 6 | Slides | Empirical Example | Codes | Smoke Data | Video | EZ Data | ||||||
Lecture 7 | Slides | Empirical Example | Fertility Data | Codes | Hotel Data | Empirical Example 2 | Codes | IBM Data | Empirical Example 3 | |||
Empirical Project | Instruction | School Data |
Lecture 1 | Slides | Codes | Video 1 | Video 2 | Video 3 | R Book 1 | R Book 2 | |||||
Lecture 2 | Slides | Codes | HW 1 | House Data | Video1 | Video2 | Old Exam 1 | Key | ||||
Lecture 3 | Slides | Codes | HW 2 | Video1 | Video2 | MiniCodes | ||||||
Lecture 4 | Slides | Codes | Math | Video1 | Old Exam 2 | Key | ||||||
Lecture 5 | Slides | Codes | HW 3 | Wage Data | Video 1 | Video 2 | NBA | NBA Data | ANOVA | |||
Lecture 6 | Slides | Codes | Gun | Trade | Nuclear | HongKong | ||||||
Lecture 7 | Slides | Codes | Video | Old Exam 3 | Key | Smoke Data | ||||||
Project | Instruction | School Data | Basic R | |||||||||
Optional Reading | NCAA | RTM | LasVegas | 1.96 | CLT | SECI | OSTT | TSTT | N Test | TIE | Log | |
Optional Reading | Geometry | LAD | COVID | WLS | Bootstrap | Matrix | 315 | Cluster | IV | Panel Data | ML | Bayes |
Optional Reading | Logistic | MLR | EventStudy | Identification | GLS | CatchMe | Cox | Text | Tree | TR | Latex | |
Data Analysis | Stock | Data | CoefPlot | Data | RD | Data | PanelDID | ESR | SC | SL | Shiny |