ECON 6113 Cross Section and Time Series Econometrics Fall 2012

Transcription

1 ECON 6113 Cross Section and Time Series Econometrics Fall 2012 Instructor: Dr. Zongwu CAI Phone: (704) Office: 340M Fretwell Office Hours: T, F, 11:00-12:00; by appointment Class Room: Center City Building 502 Class Meets: T 5:30 pm - 8:15 pm Teaching Assistant: George (Xianzhe) Chen; xchen30@uncc.edu Prerequisites: Consent of the department. Course Objective: This class introduces the advanced study of the theory and application of statistics to economic and financial problems. Topics include derivation of the least-squares estimator; methods with which to detect and correct for potential problems with the classical regression model; maximum likelihood estimation; the possible problems in regression such as of multi-colinearity, heteroscedasticity, and autocorrelation; introduction to time-series estimation. Lectures will be used to introduce the mechanics of econometric analysis and I will provide in-class examples of how to use R (which you will be able to follow if you wish to bring a lap-top or thumb drive to class). Practical learning of econometrics is accomplished through several applied out-ofclass exercises. In order to complete the applied homework, use of a statistical software package which can perform matrix and/or regression operations is required. R is the recommended software package, which can be downloaded from for free. I encourage you to download it and install it in your own computer. There are a number of alternative econometrics or statistical packages that can be used, e.g., SAS, STATA, GAUSS, RATS, and SPSS. However, if you choose one of these alternatives I will not answer questions concerning it. Textbook: The text used is Basic Econometrics, 5th edition, by Damodar N. Gujarati and Dawn Porter. A previous edition of the book should be sufficient for the class. I strongly recommend using R. Alternative textbooks include William Greene's Econometric Analysis, or Jeffrey Wooldridge's Economic Analysis of Cross Section and Panel Data. Two undergraduate texts that provide good explanations of many topics covered in this class are Pindyck and Rubenfeld's Econometric Models and Economic Forecasts and Wooldridge's Introductory Econometrics. Homework: Homework is assigned for each class and it will be collected weekly. But my TA will only choose randomly some assigned problems for grading. Please submit your homework to TA directly in an e-version by (due the midnight of Tuesday) [Coding is not needed for submission]. Please submit the file in "your_name_hw?.pdf" format. It is your full responsibility to do all problems in the homework assignment, which, definitely, benefits your test and final. If you have any questions regarding homework, please come to my office at the office hours. No late homework is acceptable for any reason.

2 Midterm Test: There will be only one midterm test and it will be on Tuesday, October 16. The test is closed-book. However, you may choose to prepare a formula sheet for reference for the test. No missed midterm test can be made up for any reason. Final Exam: A two and half hours comprehensive final examination will take place on 5:00pm - 7:30pm on Tuesday, December 11. This is a closed-book examination and a formula sheet and all necessary tables will be provided for the final examination. Grading: Grading will proceed in the following manner: Assignment Total Value Homework assignments 150 points 1 Term Paper 100 points 1 Midterm Exam 100 points 1 Final Exam 150 points 500 points Letter grades will be awarded as follows (after standard rounding): A: B: C: D: F: Attendance: There is no attendance policy in this class. You are free to attend or not attend class, this is your decision. However, attendance is a major factor in how well you will perform in the class. No points are artificially added or subtracted based on attendance. I appreciate you arriving on time and not leaving class early. If you miss class, you should NOT ask me about what you missed. It is your responsibility to get this information from one of your classmates. Academic Honesty: Please note that academic misconduct (cheating) will NOT be tolerated. In addition, students have the responsibility to know and observe the requirements of The UNC Charlotte Code of Student Academic Integrity. This code forbids cheating, fabrication or falsification of information, multiple submissions of academic work, plagiarism, abuse of academic materials, and complicity in academic dishonesty. Academic evaluations in this course include a judgment that the students work is free from academic dishonesty of any type; and grades in this course therefore should be and will be adversely affected by academic dishonesty. Students who violate the code can be expelled from UNC Charlotte. The normal penalty for a first offense is zero credit on the work involving dishonesty and further substantial reduction of the course grade. In almost all cases, the course grade is reduced to F. Copies of the code can be obtained from the Dean of Students Office. Standards of academic integrity will be enforced in this course. Students are expected to report cases of academic dishonesty to the course instructor. If in doubt when contemplating an action, ask me first!! Cell Phones: All beepers, pagers and cell phones must either be turned off prior to class starting or placed in silent mode. The proliferation of cell phones and other communication devices does not mitigate the negative externalities imposed on others when they activate during class. Laptops: The use of laptops and desktop computers in this class will be restricted to those times when we work through in-class examples using R. Statement of Diversity: The Belk College of Business strives to create an inclusive academic climate in which the dignity of all individuals is respected and maintained. Therefore, we celebrate diversity that includes, but is not limited to ability/disability, age, culture, ethnicity, gender, language, race, religion, sexual orientation, and socioeconomic status.

3 Course Outline (Subject to Change) 1. Introductory Comments (Chapter 1) 2. Statistics Review (Appendix A) 3. Simple Regression Model (Chapters 2 and 3) 4. Classical Regression Model (Chapters 7 and 8, Appendix B and Appendix C) 5. Hypothesis Testing (Chapters 5 and 8) 6. Functional Forms and Dummy Variables (Chapters 6 and 9) 7. Possible Problems in Regression (Chapters 10 and 13) 8. Generalized Least Squares (Handouts) 9. Heteroscedasticity (Chapter 11) 10. Autocorrelation (Chapter 12) 11. Limited Dependent Variables (Chapter 15) 12. Time Series Analysis (Chapters 21 and 22) 13. A Practitioner's Guide/Review of Econometrics (Handouts) Important class dates: No Class: Monday (Labor Day, September 3), Monday and Tuesday (Fall Break, October 8 and 9), Wednesday and Thursday (Thanksgiving Break, November 21 and 22) Last Day to Drop with W: Monday, November 19 Last Day of Class: Wednesday, December 5 Midterm Exam: Tuesday (October 16) Final Exam: Tuesday, December 11 (5:00PM-7:30 but subject to change) (Term paper due) Term Paper Guidelines In this class, each student is required to write a short paper involving econometric analysis. The paper is an opportunity to apply the econometric tools learned in class to a real-world issue chosen by the student. There are two approaches available for the term paper. The first is for you to choose your own topic and gather your own data. This is a bit more time consuming but can also be more rewarding (personally and professionally). The alternative is to analyze a data set that I will provide (later in the semester). This approach requires a bit more time but also requires you to propose your own model within the confines of the data provided. Regardless of the approach taken, I recommend that you choose a topic in which you are interested but also one with a narrow focus. A narrow focus increases the probability that the project will both be completed by the semester's end and be of sufficient quality. I recommend you begin thinking about this project as soon as possible and to avoid putting off writing the paper until the last few days of class. A good strategy is to talk to me about your project early in the semester, to keep in contact with me concerning your data and estimations, and to have me review a rough draft before the final draft is submitted. The final version of the term paper is due at the beginning of the final exam period on December 11, 2012.

4 There are a few guidelines that you must follow: 1. Papers should be at least 10 double-spaced, single-sided pages printed no greater than 12 font; 2. Papers should be generally structured in the following manner: o Introduction of the economic/econometric problem - what are you doing and why do we care? o Brief review of previous literature dealing with your problem (include standard academic references) o Introduction of your econometric model and data, including specific data source(s) o Review and interpretation of your estimation results o Concluding remarks o Reference list o Econometric Results in tabular form o Figures 3. You must provide an electronic form of your data, programs, program output and paper. If I do not receive all required files, you will receive a zero on the term paper. How to Install R? The main package used is R, which is free from R-Project for Statistical Computing. To Install R, you do the followings: (1) Go to the web site (2) Click CRAN; (3) Choose a site for downloading, say ; (4) Click Download R for Windows; (5) Click base; (6) Click Download R for Windows (Version of ) to save this file first and then run it to install (Note that the setup program is 32/64 megabytes and it is updated every three months). You can re-install it every three months. The above steps install the basic R into your computer. If you need to install other packages, you need to do the followings: (7) After it is installed, there is an icon on the screen. Click the icon to get into R; (8) Go to the top and find packages and then click it; (9) Go down to Install package(s)... and click it; (10) There is a new window. Choose a location to download packages, say USA(CA1), move mouse to there and click OK; (11) There is a new window listing all packages. You can select any one of packages and click OK, or you can select all of them and then click OK. I encourage you to download the file r-notes.pdf (109 pages) which can be downloaded from my home page at zcai/r-notes.pdf and learn it by yourself. Please

5 see me if any questions. Base R ships with a lot of functionality useful for computational econometrics, in particular in the stats package. This functionality is complemented by many packages on CRAN. It can be downloaded from the web site at For whatever it is worth, the links below are great resources for learning and/or ramping up on R and other statistical software packages: and There are many good examples, code snippets and ebooks that are available to ramp up on R at this site. Hope this is useful to you Partial HW Assignments for ECON6113 Chapter 2: 2.9; 2.13; 2.15; 2.16 Chapter 3: 3.16; 3.17; 3.20; 3.22 Chapter 5: 5.5; 5.12; 5.16; 5.19 Chapter 6: 6.11; 6.19; 6.20 Chapter 7: 7.13; 7.14; 7.19; 7.21; 7.24 Chapter 8: 8.5; 8.9; 8.14; 8.25; 8.28; 8.35 Chapter 9: 9.3; 9.12; 9.16; 9.24; 9.28 Chapter 10: 10.8; 10.24; Chapter 11: 11.2; 11.6; 11.15; Chapter 12: 12.6; 12.7; 12.8; 12.9; 12.10; 12.17; 12.34; 12.35; Chapter 13: 13.4; 13.5; 13.8; 13.9; 13.19; 13.26; 13.27; Chapter 15: 15.3; 15.7; 15.15;15.19; 15.20; Chapter 21: 21.16; 21.17; 21.18; 21.19; 21.20; 21.21; Chapter 22: 22.11; 22.12; 22.13; 22.14; 22.15; 22.23; More will be assigned later.