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Baruch College – CUNY Zicklin School of Business Department of Economics and Finance
Syllabus
ECO 4000 - Statistical Analysis for Economics and Finance
Instructor: Marius Mihai Office: 10 - 260A Marius.Mihai@baruch.cuny.edu Office Hours: T - 10:30 to 11: Class Time: T, R - 9:05 to 10: COURSE PREREQUISITES: STA 2000 COURSE DESCRIPTION The course provides an introduction to the econometric techniques useful to conduct empirical analysis in economics and finance. The purpose of the course is to enable the student to master the concepts and to be able to complete independently and critically an empirical project. COURSE LEARNING GOALS Firms, governmental or non-governmental agencies, regulators, experts, etc., all rely more and more on data analysis to assess situations and take decisions. Statistical analysis and, in particular, econometrics offers powerful tools that are relatively easy to use but that need to be used properly. Interpreting correctly results from a statistical analysis is also paramount to the discipline. By the end of the semester students will be able to:
- Handle data in a professional manner
- Know how to use different statistical programs and know which one is the most appropriate for the type of study.
- Recognize the main pitfalls encountered in statistical analysis.
- Develop knowledge of the basic principles of probability and statistics
- Develop understanding of the Linear Regression Model (LRM) and its use in modeling the relationship between economic and financial variables
• Be able to estimate and test hypothesis about the parameters of the LRM
• Be able to conduct an empirical investigation using econometric techniques
- Be able to present the results of a statistical analysis. BBA PROGRAM-LEVEL LEARNING GOALS Quantitative Thinking Skills Students will apply the quantitative thinking and the mathematical modeling process to solve real- world problems Technological Skills Students will be proficient in appropriate software to solve problems in statistics and quantitative modeling Data Analysis Students will be able to identify appropriate methodology, conduct analysis, and interpret results. Communication Skills Students will effectively communicate statistical and quantitative modeling methods for decision making to technical and non-technical audiences
Probabilistic Modeling Methods Students will be able to model probabilistic problems dealing with decision analysis and simulation Statistical Modeling Students will be able to model statistical problem applied to business COURSE MATERIALS James Stock and Mark Watson, Introduction to Econometrics, 4th Edition, Pearson. We have a customized edition for the course which includes: a paperback copy of the book (once you have registered to myEconlab), access to myEconlab and e-book (including the possibility of reading the book on any tablets). You are required to own a copy of the textbook. All of this can be purchased from Pearson when you register for myEconlab. The package is also
available in the bookstore with the ISBN # 9780136412151 and it costs $80. Also, if you decide to drop this class,
refunds are processed through the source you used to purchase the package. Below I copied the link from Pearson’s refund policies:
https://support.pearson.com/getsupport/s/article/Refund-Requests
If you buy it in the bookstore, please check their refund policy as it is different than the one Pearson has.
Econometrics is a major component of economics and finance. There are dozens of other books available (plus websites, Wikipedia, YouTube videos, etc.). I will use personalized slides. Students having some doubts about the adequacy of a specific source should discuss with the instructor. DELIVERABLES/COURSE ASSIGNMENTS There will be online assignments, two midterm exams and a final exam.
- Assignments o Online (via Blackboard) o 6 - 8 assignments during the semester (depending on the amount of material we cover). They will be announced in class so attendance is mandatory. o Students will have about 1 week to complete them once I make the announcement. o Deadlines to submit the answers of an assignment are hard : After the deadline students will not be able to submit their answers. o A student not having submitted his or her answers for an assignment will get a grade of 0 for that assignment. o Technical issues (internet connection broke down, the server didn’t save my answers, etc.) will not be accepted as excuses. Students should not wait the last day to submit their assignments.
- Applied Assignment in R or Excel o Later on during the semester I will discuss what will be covered in the project. o Its purpose is to get students started into basic econometric analysis using R software. o However, you are free to use Excel instead of R. o I will hold one lecture demonstrating how to set it up and talking about my expectations from the project.
- Midterms o Both in class; you will get the full 1h and 15mins. o Midterm 1 Date: Beginning of March. o Midterm 2 Date: Beginning of April. o A student not having attended the midterm will get a grade of 0.
- Final Exam o Date and venue decided by the school o Final Exam will cover material from Midterm 2 (including) onward (partially cumulative).
Extra Credit No student will be able to improve his or her grade with an extra work or assignment. Grades will depend only on the assignments, the empirical project, the midterm exams and the final exam. This implies that other factors (graduation GPA, number of credits left to graduate, type of major or minor, etc.) will be ignored when calculating grades. Requests for extra credit from students who email me after I post the final letter grade on Blackboard will be ignored. Grade change Requests for grade lowering (e.g., from D+ to F) will be denied. POLICY REGARDING MAKE-UP EXAMS AND DEADLINE EXTENSIONS Students are responsible for checking the exam dates and avoid any conflict with other commitments. There will be Make-ups for exams except only if:
- The student has contacted the instructor before the exam and the instructor has agreed to organized a make-up exam (interviews or business trips do not constitute a valid excuse to have a makeup).
- There is a case of documented serious illness or civic obligation Solutions to assignments will be available immediately after the deadline. So no deadline extension will be provided, under any circumstance. CLASSROOM MANAGEMENT POLICIES Lectures are for academic purposes. Students are not supposed to use class time to chat, surf the Internet, eat/drink, etc. It is important to pay close attention in class to problems that I work on the board because those represent fair material for assignments and exams. Print lecture slides before class and take notes as I go over concepts during lecture. BLACKBOARD WEBSITE Please check Blackboard regularly since all documents, announcements and others will be posted there. ATTENDANCE AND LATENESS POLICIES: There will be an attendance sheet that will circulate during class and retrieved by the instructor at the end of the lecture. Do not arrive late but if you are in a situation when you arrive after my lecture starts please walk in quietly and take a seat in the back of the class so you don’t disturb others. Showing up in the middle of the lecture and writing your name on the signing sheet will not be permitted. STUDENT DISABILITIES We have a process at Baruch for determining whether a student who identifies as disabled is eligible for reasonable accommodations in order to complete the student’s academic program. We strive to ensure that no student with a disability is discriminated against and that none is denied participation in College programs and activities for lack of reasonable accommodations. Some people think that a disability has to be visible to be accommodated. This is not the case. There are many disabilities – diabetes, psychological illness, learning disabilities, AIDS, seizure disorders, arthritis, etc., – that require accommodations.
Examples of possible accommodations include additional testing time; adaptive equipment; and taping of classes. If you feel that you may need a reasonable accommodation based on a disability, please contact the staff at the Office of Disability Services, Newman Vertical Campus, Room 2-271, or by phone at (646) 312-4590.
ACADEMIC INTEGRITY
I fully support Baruch College's policy on Academic, which states, in part: "Academic dishonesty is unacceptable and will not be tolerated. Cheating, forgery, plagiarism and collusion in dishonest acts undermine the college's educational mission and the students' personal and intellectual growth. Baruch students are expected to bear individual responsibility for their work, to learn the rules and definitions that underlie the practice of academic integrity, and to uphold its ideals. Ignorance of the rules is not an acceptable excuse for disobeying them. Any student who attempts to compromise or devalue the academic process will be sanctioned. " Additional information can be found athttp://www.baruch.cuny.edu/academic/academic_honesty.html[ Students caught cheating will receive a PEN grade and a report of suspected academic dishonesty will be sent to the Office of the Dean of Students. ASURANCE OF LEARNING BBA Common Educational Learning Goals Significant Part of Course Moderate Part of Course Minimal Part of Course Not Part of Course Quantitative Thinking Skills X Technological skills X Communication skills X Data Analysis X Statistical Modeling X Probabilistic Modeling Methods X ASSIGNMENT MAPPING Assignments Course Learning Goals BBA Common Learning Goals Class Lectures and Participation
- Know how to use different statistical programs and know which one is the most appropriate for the type of study.
- Recognize the main pitfalls encountered in statistical analysis.
- Develop knowledge of the basic principles of probability and statistics
- Develop understanding of the Linear Regression Model (LRM) and its use in modeling the relationship between economic and financial variables Quantitative Thinking Skills Technological skills Statistical Modeling Probabilistic Modeling Methods In-Class Exercises
- Handle data in a professional manner
- Know how to use different statistical programs and know which one is the most appropriate for the type of study.
- Recognize the main pitfalls encountered in statistical analysis. Quantitative Thinking Skills Technological skills Communication skills Data Analysis Probabilistic Modeling Methods
4. Chapter 4 : 3 / 5 , 3 / 10 , 3 / 12 , 3/
4.1. The Linear Regression Model (LRM)
4.2. Estimation of LRM
4.3. Measures of Fit (MOF)
4.4. Least Squares (LS) Assumptions
4.5. Sampling Distribution of the OLS Estimators
5. Chapter 5 : 3 / 1 9, 3 / 24
5.1. Testing Hypothesis about One Regression coefficient
5.2. Confidence Intervals for a Regression coefficient
5.3. Regression with a Binary variable
5.4. Heteroskedasticity and Homoskedasticity
5.5. Theoretical Foundations of OLS estimators
Review Chapters 4 , 5 : 3 / 26
Midterm 2: 3/
6. Chapter 6 : 4 /2, 4 / 21
6.1. Omitted Variable Bias
6.2. The Multiple Regression Model
6.3. The OLS Estimator in Multiple Regression Model
6.4. MOF in Multiple Regression
6. 5. LS Assumptions in Multiple Regression Model
6.6. Distribution of OLS estimators in Multiple Regression Model
6.7. Multicollinearity
7. Chapter 7 : 4/23, 4 /2 8
7.1. Hypothesis Tests and Confidence Intervals in Multiple Regression Models
7.2. Tests of Joint Hypotheses
7.3. Test of Single Restrictions involving Multiple Coefficients
7.4. Confidence Sets Multiple Coefficients
7.5. Model Specification for Multiple Regression
8. Chapter 8 : 4/30, 5 /5, 5 / 7 , 5/
8.1. Modeling Nonlinear Regression Functions
8.2. Nonlinear Functions of a Single Independent Variable
8.3. Interactions between Independent Variables
Final Exam Review 4 ,5,6,7,8 : 5 /1 4
Final Exam: TBA
