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Course Code: 
AEC 7202
Course Credit Units: 
Semester 1
Year of Study: 
Year 1
Undergraduate or Graduate Level: 
Graduate Level
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.
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