# Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

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1 Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Telephone Ext Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics Fall Semester 2017 COURSE BASICS Credit Hours 4 Lecture(s) Nbr of Lec(s) Per Week 2 Duration 100 Recitation/Lab (per week) Nbr of Lec(s) Per Week 1 Duration 60 Tutorial (per week) Nbr of Lec(s) Per Week Duration COURSE DISTRIBUTION Core Elective Open for Student Category Close for Student Category Yes Sophomore/Junior/senior Lacking the prerequisite COURSE DESCRIPTION Overall Theme This is the second course in the statistics/econometrics sequence and looks at the broad range of estimation problems that often arise in economic applications. In particular, we look at the criteria used to select a particular estimation method and the scenarios under which the OLS estimator becomes sub optimal. The purpose of this course is to teach students econometric theory and also to give them hands on experience with using a statistical package Stata EXCEL, and Eviews, which will be helpful in later applications especially for those students who choose to do an empirical senior project. COURSE PREREQUISITE(S) [DISC 203 Probability & Statistics (OR) MATH 230 Probability (AND) MATH 231 Statistics (OR) ECON 230 Statistics & Data Analysis] (AND) ECON 111 Principles of Microeconomics COURSE LEARNING OBJECTIVES On successful completion students will: 1. Be able to develop a suitable regression model for a variety of empirically interesting problems and validate the selected model via a battery of tests. 2. Be able to compare different estimators based on their finite sample and asymptotic properties. 3. Develop a basic understanding of the use of dummy variables also learn the use of Probit, Logit, and Tobit models. 4. Develop a basic understanding of time series econometrics and its application in the field of finance. 5. Be able to estimate and interpret dynamic models and have experience with simultaneous equation models. 6. Be proficient in the use of the statistical software Stata Eviews and Excel.

3 Major Specific Learning Goals & Objectives Goal 7 (a) Discipline Specific Knowledge and Understanding Objective: Students will demonstrate knowledge of key business disciplines and how they interact including application to real world situations (Including subject knowledge). Goal 7 (b) Understanding the science behind the decision making process (for MGS Majors) Objective: Students will demonstrate ability to analyze a business problem, design and apply appropriate decision support tools, interpret results and make meaningful recommendations to support the decision maker Indicate below how the course learning objectives specifically relate to any program learning goals and objectives. PROGRAM LEARNING GOALS AND OBJECTIVES Goal 1 Effective Written and Oral Communication Goal 2 Ethical Understanding and Reasoning Goal 3 Analytical Thinking and Problem Solving Skills Goal 4 Application of Information Technology Goal 5 Teamwork in Diverse and Multicultural Environments Goal 6 Understanding Organizational Ecosystems Goal 7 (a) Discipline Specific Knowledge and Understanding Goal 7 (b) Understanding the science behind the decision making process COURSE LEARNING OBJECTIVES e.g(provide student opportunity to demonstrate effective communication) CLO # Class participation Home work will be based on Excel Stata, and Eviews Programming Exams Exams COURSE ASSESSMENT ITEM Quizzes and homework Quizzes+mid+final Quizzes+mid+final GRADING BREAKUP AND POLICY Assignment(s) 10% Quiz(s) 20% Class Participation 5% Mid Term 25% Final 40%

4 EXAMINATION DETAIL Midterm Exam Yes/No: YES Combine Separate: Combine Duration: 120 minutes Preferred Date: Exam Specifications: closed books and closed notes Final Exam Yes/No: YES Combine Separate: Combine Duration: 120 minutes Exam Specifications: closed books closed notes COURSE OVERVIEW WEEK 1 2 TOPICS Introduction What is econometrics? Steps in empirical economic analysis The structure of economic data; random sampling Two Variable Regression Analysis Basic ideas The concept of PRF The concept of Linearity The concept of SRF Two Variable Regression Analysis: Estimation RECOMMENDED READINGS * THE READINGS ARE ALL FROM THE TEXTBOOK UNLESS OTHERWISE INDICATED Chapters.1,2 Chapters. 2,3,4 2, Mechanics and Interpretation of OLS and Method of Maximum Likelihood (ML) Classical Linear Model Assumptions The Gauss Markov Theorem Properties of OLS Mean and Variance Goodness of fit The normality assumption Two Variable Regression: Interval Estimation and Hypothesis Testing Construction of confidence interval and Testing of hypothesis related to regression coefficients and variance. The science of p values. Extension of the Two Variable linear Regression Model Regression through origin, scaling and units of measurements, functional form of the model and log linear models estimation Multiple Regression Analysis: The problem of Estimation and Multicollinearity The three variable model The meaning of Partial Regression coefficients Concept of R square and testing of hypothesis Chapter. 5 Chapter.6 Chapter 7,8,9

5 Heteroskedasticity Consequences of Heteroskedasticity Robust inference Testing for heteroskedasticity Weighted Least Squares Autocorrelation OLS estimation in the presence of Autocorrelation; consequences and testing of autocorrelation; Remedial measures to autocorrelation Regression on Dummy Variables The nature of Dummy variables; regression with dummy variables and testing of hypothesis and doing analysis such as gender differences, structural change and regime change etc. The regression on Dummy Dependent Variable: The LPM, Logit, Probit, and Tobit Models Dummy dependent variable, The linear probability model (LPM); estimation and drawing inferences from such models Chapter 11 Chapter 12 Chapter 15 Chapter Dynamic Econometric Model: Autoregressive and Distributed Lag Models Estimation of distributed lag models; Koyck approach to distributed lag models, adaptive expectations stock adjustment and partial adjustment models; estimation of AR models Introduction to Time series Econometrics Stationary vs. non stationary process; test of stationarity (Dickey Fuller Test); trend stationarity and difference stationarity; conintegration and error correction model. Simultaneous Equation Models The nature of Simultaneous equation models; the simultaneous equation bias, the identification problem; test of simultaneity; approaches to estimation Chapter 17 Chapter 21 Chapter 18,19,20 TEXTBOOK(S)/SUPPLEMENTARY READINGS Readings: Text Book Basic Econometrics by Damodar Gujrati. 3 rd edition or latest edition. McGraw Hill 1995 Reference Texts 1. Hamilton, Lawrence C Statistics with Stata. Thomson Brooks/Cole. 2. Kennedy, Peter A Guide to Econometrics. 6 th edition. Malden: Blackwell Publishing. Levitt, Steven D., and Stephen J. Dubner Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. Harper Perennial Online Resources To learn STATA you may use: STATA illustrations for all our text book examples are at: The power point slides for the book are also available at:

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