# ECONOMETRICS

Course Code:
AEC 7202
Course Credit Units:
3
Semester:
Semester 1
Year of Study:
Year 1
Course Discipline:
Course Description & Objectives:

The purpose and applications of Econometrics; kinds of problems addressed by econometrics; the link between economic theory, mathematics, and statistics; review of regression model essentials (assumptions, parameter estimates, goodness-of-fit); univariate and multivariate ordinary least squares; addressing problems due to heteroskedasticity, autocorrelation, multicollinearity, specification errors; dummy independent and dummy dependent variables; binary and multinomial probit and logit models; models of count data (poisson and negative binomial models); limited dependent variable models (tobit model);  sample selection bias, selectivity problem, Heckit (Heckman model), self selection, double huddle model, matching estimators (propensity score matching); endogenous and exogenous variables; reduced form equations; identification problem; estimation of simultaneous equations (maximum likelihood and two-stage least squares); recursive models; seemingly unrelated regressions equations.                                                                                                                                                                                                                                                                objectives :

• Equip students with Econometric theory and application to test economic principles and to quantify and test economic relationships;
• Provide students with knowledge to apply modelling, estimation, inference and forecasting in the context of real world economic problems;
• Enable students to translate results from econometric analysis based on economic principles into useful and reliable policy recommendations;
• Enable students to read, evaluate and understand empirical papers in professional journals; and
• Provide students with practical experience in using statistical/econometric computer software to analyse economic models.

Learning Outcomes:

By the end of the course students should:-

• Have solid understanding of various regression models, their underlying assumptions and the consequences of violating them;
• Understand how to specify an econometric model and conduct the necessary diagnostic and specification tests;
• Have an appreciation of the range of more advanced econometric techniques that exist to date;
• Be able to conduct their own empirical investigations and critically evaluate econometric and other statistical estimations and inferences; and
• Understand econometric literature addressing economic issues and be able to critically review their methodology and interpretation of results.
File Attachments: