This course focuses on learning and practicing basic econometrics with emphasis on the practice and less emphasis on deep econometric theory. Econometrics is a subfield of the economics discipline that mixes together economic theory, statistics and mathematics. The main purposes of the course are to introduce econometric theory at a very basic level and to let you apply the methods with the use of real world data.
The objective of the course is for the student to learn how to conduct – and how to critique – empirical studies in economics and related fields. Accordingly, the emphasis of the course is on empirical applications. The mathematics of econometrics will be introduced only as needed and will not be a central focus.
The applied work will be performed using econometric software package called EViews 4.1 Student Version software. Alternative econometric software packages (gretl, JMulti, and X-12-ARIMA) can also be downloaded for free at http://basiceconometrics.blogspot.com/.
Periodically, the instructor will post news and information in the class blogspot. Students are responsible for ensuring that they will check daily this web page. The homework and the empirical project should be worked on groups of no more than three students. These groups should make a presentation of their results in the last week of the semester. Midterm and final examinations will be administered. Exams will cover all the topics discussed in class plus the techniques applied in homework assignments.
Attendance will be part of the grade. For each class missed students will loss 10% of the grade assigned for attendance. Students could miss two classes without justification.
Homework assignments will be available in the blogspot. The schedule for the due dates of each assignment will be announced in class and posted on the blogspot.
Students should be able to complete an empirical work applying the econometric tools presented in class. Groups have to define an economic or business issue, find the data to test it and finally apply the econometric tools to explain their hypothesis. Further information about the requirements for the paper will be provided in the class blogspot.
Working together is a wonderful way to learn, and your instructor encourages it. You will be encouraged to work with others in this class on any activity except examinations. All examinations, whether in-class or take-home, must be individual efforts. Plagiarism is taking someone else’s work and passing it off as your own. Plagiarism includes taking phrases, sentences, or paragraphs from someone else’s writing and using them in your own writing without providing true attribution of their source. Avoiding plagiarism, of course, does not mean neglecting to conduct solid research. It is appropriate to read what various scholars and experts have learned about an issue before you form your own conclusions about it.
However, you must ensure that you understand the literature. At a minimum, students should rephrase the literature’s content, rather than quoting it verbatim. This practice also helps to ensure student understanding of the issue, as you cannot write intelligently unless you do know your subject. Another way to avoid plagiarism is to ensure that you utilize a large number of sources, so that your knowledge goes beyond that of any particular book or article.
Students are expected to remain polite during classroom discussions. Even during heated debates, you must treat your classmates with respect. Violation of this policy will result in a reduction of your class participation grade. For example, you should not make derogatory remarks about your classmates’ ideas. Instead, explain why you think they are wrong, backing up your viewpoint with sound economic analysis and refrain from personal attacks. Another example is being quiet while someone else (including your instructor!) has the floor.
You may not use cellular telephones in class. If you bring them to class, they must be turned off. If there is an emergency situation that requires you to have an active telephone in class, you must notify your instructor in advance begins that your equipment will be turned on. In such cases, cell phones, if possible should be set to vibrate, not to sound an alarm.
PART ONE INTRODUCTION AND REVIEW
Chapter 1 Economic Questions and Data
1.1 Economic Questions We Examine
1.2 Causal Effects and Idealized Experiments
1.3 Data: Sources and Types
Chapter 2 Review of Probability
2.1 Random Variables and Probability Distributions
2.2 Expected Values, Mean, and Variance
2.3 Two Random Variables
2.4 The Normal, Chi-Squared, Student t, and F Distributions
2.5 Random Sampling and the Distribution of the Sample Average
2.6 Large-Sample Approximations to Sampling Distributions
Chapter 3 Review of Statistics
3.1 Estimation of the Population Mean
3.2 Hypothesis Tests Concerning the Population Mean
3.3 Confidence Intervals for the Population Mean
3.4 Comparing Means from Different Populations
3.5 Differences-of-Means Estimation of Causal Effects
3.6 Using the t-Statistic When the Sample Size Is Small
3.7 Scatterplot, the Sample Covariance, and the Sample Correlation Using Experimental Data
PART TWO FUNDAMENTALS OF REGRESSION ANALYSIS
Chapter 4 Linear Regression with One Regressor
4.1 The Linear Regression Model
4.2 Estimating the Coefficients of the Linear Regression Model
4.3 Measures of Fit
4.5 The Sampling Distribution of the OLS Estimators
Chapter 5 Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
5.1 Testing Hypotheses About One of the Regression Coefficients
5.2 Confidence Intervals for a Regression Coefficient
5.3 Regression When X Is a Binary Variable
5.5 The Theoretical Foundations of Ordinary Least Squares
5.5 The Theoretical Foundations of Ordinary Least Squares
5.6 Using the t-Statistic in Regression When the Sample Size Is Small
Chapter 6 Linear Regression with Multiple Regressors
6.1 Omitted Variable Bias
6.2 The Multiple Regression Model
6.3 The OLS Estimator in Multiple Regression
6.4 Measures of Fit in Multiple Regression
6.5 The Least Squares Assumptions in Multiple Regression
6.6 The Distribution of the OLS Estimators
Chapter 7 Hypothesis Tests and Confidence Intervals in Multiple Regression
7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient
7.2 Tests of Joint Hypotheses
7.3 Testing Single Restrictions Involving Multiple Coefficients
7.4 Confidence Sets for Multiple Coefficients
7.6 Analysis of the Test Score Data Set
Chapter 8 Nonlinear Regression Functions
8.1 A General Strategy for Modeling Nonlinear Regression Functions
8.2 Nonlinear Functions of a Single Independent Variable
8.4 Nonlinear Effects on Test Scores of the Student–Teacher Ratio
Chapter 9 Assessing Studies Based on Multiple Regression
9.1 Internal and External Validity
9.2 Threats to Internal Validity of Multiple Regression Analysis
9.3 Internal and External Validity When the Regression Is Used for Forecasting
9.4 Example: Test Scores and Class Size
Chapter 10 Conducting a Regression Study Using Economic Data
10.1 Choosing a Topic
10.2 Collecting the Data
10.3 Conducting Your Regression Analysis
10.4 Writing Up Your Results