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Econometrics

Econometrics. Course Syllabus. Personal Information. Instructor Name: Ming-Yuan (Leon) Li ( 黎明淵 ) Instructor Tel: Ext 53421 E-mail: lmyleon@mail.ncku.edu.tw Office Hours: 10:00 AM-12:00 PM, Wednesday. Academic Experience. Current Position:

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Econometrics

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  1. Econometrics Course Syllabus

  2. Personal Information • Instructor Name: Ming-Yuan (Leon) Li (黎明淵) • Instructor Tel: Ext 53421 • E-mail: lmyleon@mail.ncku.edu.tw • Office Hours: • 10:00 AM-12:00 PM, Wednesday

  3. Academic Experience • Current Position: • Professor of Finance, Department of Accountancy and Institute of Finance and Banking, National Cheng Kung University (NCKU), Taiwan • Past position: • Associate Professor and Chair, Department of Banking and Finance, National Chi Nan University (NCNU), Taiwan

  4. Research interests • Applied financial econometrics • Money and banking • International portfolio management • Risk management • Stochastic volatility

  5. Course Descriptions/Objectives • The goals of this course are presented below： • (1) Formulation of econometrics, that is, formulation of economic (or financial) models in an empirically testable form. • (2) Estimation and testing of these models with observed data • (3) Use of these models for prediction and policy purposes

  6. Writing the dissertation • The process of writing the dissertation • Variables/hypotheses establishment • Model establishment • Data collection • Model estimation • Explanations and discussions • Materials for research

  7. Grading • Examinations (60%) • Midterm Examination (the 9th week) • Final Examination (the 18th week) • Assignments (20%) • Participation rate (20%)

  8. Grading • Participation rate • Since It is a graduate student training course, you are encouraged to participate actively in classroom discussion. In order to maximize your learning and to receive credit for your classes, you must attend at least 80% of classes. • Teacher will follow the textbook to present the related important topics of Econometrics. It is expected that every student attend all classes and take all examinations when scheduled.

  9. Textbook • Maddala, G.S., Introduction to Econometrics • Two Supplements • Hamilton, J.D., Time Series Analysis • Judge, G.G. et. al., Introduction to the Theory and Practice of Econometrics

  10. Course Calendar/Schedule • The Midterm Examination: • The Linear Regression Model (Ch 2 to Ch 4) • Chapter 5 Heteroskedasticity • Chapter 6 Autocorrelation • The Final Examination: • Chapter 7 Multicollinearity • Chapter 8 Dummy variables and truncated models • Chapter 9 Simultaneous equations models • Chapter 13 Introduction to time-series analysis • Chapter 14 Vector autoregressions, unit roots, and cointegration • Chapter 15 Panel Data Analysis

  11. Course Calendar/Schedule • Special topic: • Add an alternative dimension • Markov-switching, Threshold and Quantile models • Computer Programs • E-views, SPSS, SAS • Gauss • Stata

  12. Slides of Course • The slides in PowerPoint • My personal web-site • http://140.116.51.3/chinese/faculty/mingyuan/myweb11/index.htm • Download them before the class

  13. An Overall Picture The errors are again due to measurement errors in y and errors in the specification of the relationship between y and the x’s. We make the same assumptions about the errors:

  14. An Overall Picture • for all i. • and are independent foe all . • and are independent foe all i and j. • are normally distributed for all i .

  15. An Overall Picture • There are no linear dependencies in the explanatory variables, 7. Also, it will be assumed that is a continuous variables.

  16. An example • Y=firm performance • X1=diversification • Hypothesis: Firms can benefit from economies of scale and scope when operating in multiple industries (i.e., β1>0) • X2, X3,…,Xk= control variables (e.g., firm size, debt ratio, M/B ratio)

  17. The iid assumption?

  18. Three W’s Questions • How do we detect this problem • What are the consequences of this problem? • What are the solutions?

  19. Properties of Estimators • There are some desirable properties of estimators that are often mentioned in the book. • These are: • 1. Unbiasedness. • 2. Efficiency. • 3. Consistency. • The first two are small-sample properties. The third is a large-sample property.

  20. Course requirements • Statistics, regression analysis • Variance, standard error, covariance, correlation • Normal distribution, student t distribution • Unbiased, efficient, consistent estimates • Null hypotheses • OLS, MLE, R-square

  21. How to find a topic? • New findings (find new variables) • Test other market & International market comparison • Replacing a variable with better proxy • Decompositions • Reverse causality • Using unique data • Add an alternative dimension

  22. How to find a topic? • New finding (find new variables) • Li, Ming-Yuan Leon*, Her-Jiun Sheu, Lin Lin and Yu-Chi Tang (2007) Market conditions and abnormal return of IPO- An empirical study of Taiwan's high-tech companies, Journal of Chinese Economic and Business Studies, 5, 51-64. • Chiou, Jeng-Ren, Ming-Yuan Leon Li*, Cheng Li, Shih-Yuan Chang (2010) Changes in pricing and allocation mechanisms and abnormal returns of Chinese IPO firms, Chinese Economy, 43, 93-108.

  23. How to find a topic? • Test other market & International market comparison • Li, Ming-Yuan Leon* and Hsiou-wei William Lin (2003) Examining the volatility of Taiwan stock index returns via a three-volatility-regime Markov-switching ARCH model, Review of Quantitative Finance and Accounting, 21, 123-139. • Li, Ming-Yuan Leon* (2009) Change in volatility regimes and diversification in emerging stock markets, South African Journal of Economics, 77, 59-80.

  24. How to find a topic? • Replacing a variable with better proxy • Decompositions • Chen, Chun-Nan, Ming-Yuan Leon Li*, Yi Chou, Li-Ling Chen and Wan-Ru Liou (2010) Are large banks less risky? Service Industries Journal, accepted and forthcoming. • Reverse causality • Using unique data

  25. How to find a topic? • Add an alternative dimension • Markov-switching model • Threshold model • Quantile regression model

  26. An example • Y= Firm performance • X1= Diversification • Hypothesis 1: Firms can benefit from economies of scale and scope when operating in multiple industries (i.e., β1>0) • Hypothesis 2: Diversification is accompanied by internal governance costs when managing multi-business corporations (i.e., β1<0)

  27. An example YES! BUT… Premium? Discount? xxxxx… ooooo… ?

  28. How to find a topic? • Li, Ming-Yuan Leon and Peter Miu (2010) A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach, Journal of Empirical Finance, 17, 818-833. • Li, Ming-Yuan Leon* (2009) Value or volume strategy? Finance Research Letters, 6, 210-218 • Li, Ming-Yuan Leon* (2008) Hybrid versus highbred: Combined economic models with time-series analyses, Quantitative Finance, 10, 637-647.

  29. Email: lmyleon@mail.ncku.edu.tw Current position: Professor of Finance, Institute of Finance and Banking, National Cheng Kung University (NCKU), Taiwan Education: Doctor of Business Administration-Finance, National Cheng Chi University, Taiwan (2000). Interest: Golf, travel and bike (林欣潔：abc8227279@yahoo.com.tw ) Ming-Yuan (Leon) Li 黎明淵

  30. My Travel Experience

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