**Rutgers University**

**The State University of New Jersey**

**Department of Economics – CCAS**

**Summer 2014**

**Class Information**

**Course Title**: Econometrics (index#05179)

Economics 50:220:322/Section H6

**Instructor:** Dr. I-Ming Chiu

**Office: **ARMITAGE 328

**Phone:** (856) 225 6012

**E-mail address:** ichiu@camden.rutgers.edu

**Class Meeting: **BSB 336. 6:00-9:40 PM (Tuesday & Thursday, 07/07~08/13)

**Office Hours: **4:30-5:30 PM, Tuesday & Thursday or by appointment

**Course Description:**

Econometrics is a branch of economics. It applies mathematical and statistical methods to explore and quantify the relationship between economic variables. The class will begin with a brief review of set theory, function, random variable, commonly used probability distributions (discrete and continuous), statistical inference, and then cover the essential part of this class: regression model. In addition to introducing students to the theoretical parts of the regression model, the main focus is to show students how to apply the model using economic data. The ultimate goal of this class is to equip students with analytical ability to *explain* and *forecast* economic phenomena. With these quantitative skills in hand students will become more competitive in the job market.

**Textbook (required; either one):**

- Roberto Pedace, Econometrics for Dummies, Wiley, 2013 (available at bookstore).
- R. L. Thomas, Modern Econometrics: An Introduction, Longman U.K., 1997.

**Other References (recommended):**

- Michael W. Trosset, An Introduction to Statistical Inference and Its Applications with R, CRC Press, 2009.
- Jared P. Lander, R for Everyone: Advanced Analytics and Graphics, Pearson, 2014 (available at bookstore).
- David Poole, Linear Algebra: A Modern Introduction, 3
^{rd}Edition, Brooks/Cole, Cengage Learning, 2011.

**Computing:**

All of the computations will be done using an open source statistical software **R**. The other software package **SAS/IML** will also be introduced as an alternative to deal with matrix operations for data analysis. R can be downloaded at http://www.r-project.org.

**Class Material:**

Handouts, readings, data, and homework assignments will be posted on **Sakai** website.

**Useful Websites:**

http://www.dummies.com/how-to/education-languages/Economics/Econometrics.html (Econometrics for Dummies textbook companion website)

http://www.ats.ucla.edu/stat/ (Learn a variety of statistical software packages and statistical methods from UCLA web site)

**Economic Data:**

http://www.federalreserve.gov/econresdata/statisticsdata.htm (the Federal Reserve)

http://finance.yahoo.com (Yahoo Finance Section)

http://www.bea.gov (Bureau of Economic Analysis)

**Grading: Contribution to Final Grade**

– Attendance 5%

– Take-home problems 50%

– Midterm Exam (1) 20%

– Final Exam (or Project) 25%

– Participation (extra credit) 5%

**Grading Policy:**

Term grades will be based on the final distribution of the above grading weights.

**Exam Preparation:**

The exam questions will be drawn from three sources: (i) homework assignments, (ii) course lectures, and (iii) reading material.

**Class Participation:**

Class attendance is essential for learning achievement. When missing a class, it would cost you more time to learn on your own. I strongly recommend the following steps for your successful learning: (1) attend every class and take notes; (2) review everything you learn from the class immediately, never put it off; (3) ask questions and participate in class discussions.

**Academic conduct:**

Make up exams will be given only upon prior notice. I request prior knowledge of any expected absence from an exam. If this is not feasible, you can document a valid reason for missing the exam. Unexcused absence on any exam will result in a grade of zero. Dishonesty in seeking an excused absence or in the examination process will result in a grade of zero on the exam involved and in university discipline.

**Course Outline: **

Topic 1 |
Probability Review |

Topic 2 |
Statistical Inference Review |

Topic 3 |
Matrix Operations |

Topic 4 |
Econometrics & Statistical Learning |

Topic 5 |
Simple Linear Regression Model |

Midterm Exam |
Date: TBA |

Topic 6 |
Multiple Linear Regression Model |

Topic 7 |
Dummy Variable Regression Model |

Topic 8 |
Transformations |

Topic 9 |
Regression Diagnostics |

Topic 10 |
Logit and Probit Model |

Final Exam |
Last Day (08/12) of the Class |

Notice: there are five R lab sessions.