Office Hours: 10-1 Wednesday

Office 913 PLC, 346-4868

This course focuses on survey of econometrics techniques with emphasis on applications in political science. Specific topics include introduction to basic statistical concepts, introduction to quantitative analysis, concepts and methods of empirical research, applied statistical data analysis in political science. Methods include descriptive statistics, bivariate correlation, and regression techniques. The winter part of the course will mostly focus on theoretical aspects. Designed to help students become informed users of quantitative methods in political science.

GRADING: There will be two take-home midterm exams in this course, and no final exam. The exams will be due on Feb. 7th (50 points) and Mar. 14th (50 points). Each exam will be available at least two days before the due date. No makeups will be scheduled for any of the exams without a documented medical excuse. There will be a number of homeworks in this course. They will be graded as pass/fail. Generally a passing grade will be given to all turned in homeworks which show that some reasonable amount of work has been contributed, and at least an attempt was made to find correct answers. Each missing homework will result in a 5 point deduction from final score. Grades: A:85-100, B:70-84, C:50-69.

BOOKS: G. S. Maddala "Introduction to Econometri cs" - primary textbook

Peter Kennedy "A guide to Econometrics" - secondary textbook

Jan. 6-10 The Aims and Methodology of econometrics, Probability, Addition Rules of Probability, Conditional Probabilities and the Multiplication Rule, Summation and Product Operations. Reading: Maddala 1-17.

Jan. 13-17 Random Variables and Probability Distributions, Continuous random variables, Probability density function, Joint, Marginal and Conditional Distributions. Reading: Maddala 1-17.

Jan. 20-24 Normal probability distribution and related distributions. Reading: Maddala 18-21.

Jan. 27-31 Classical Statistical Inference, Statistical Conclusions, Bayesian Inference, Sample Precision, Prior and Posterior Distributions, Point Estimators. Reading: Maddala 21-23.

Feb 3-7 Sampling Distributions, Interval Estimation, Hypothesis Testing, Properties of Estimators: Unbiasedness, Efficiency, Asymptotic Properties. Midterm Exam (Feb 7th). Reading Maddala 24-35.

Feb 10-14 Simple Regressions, Multiple Regressions, Stochastic Relationship, Regression Coefficients, Regression Parameters, Measurement Error, Assumptions.

Feb. 17-21 The Method of Moments, Method of Least Squares, Residuals, The reverse Regression. Reading: Maddala 60-76.

Feb. 24-28 Statistical Inferences in the Linear Regression Model, Testing of Hypothesis, Regression with No Constant Term. Reading Maddala 77-83.

Mar. 3-7 Analysis of Variance for the Simple Regression Model. Prediction with the Simple Regression Model, Outliers. Reading: Maddala 84-96.

Mar. 10-14 Review, Midterm Exam (Mar 14th).