Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman
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- INTRODUCTION TO ECONOMETRICS II ECO 306
| Course Outline ECO 306 is made up of five modules with seventeen units spread across twelve lectures weeks. The modules cover areas such as theconcept of thesimultaneous equation and their estimation, ordinary least squares assumptions, multicollinearity, heteroscedasticity, autocorrelation and econometrics modeling Specification and Diagnostic Testing, use of dummy variables and time-lags as independent variables. Aims The aimof this course is to give you thorough understanding and an appreciative importance of econometrics being concerned with more than measurement in economics. But more importantly, how econometrics as a method of causal inference isapplied to economics. That is, this method of causal inference is a statistical inference combined with the logic of causal order which is to infer or learn something about the real world by analysing a sample of data. Specifically, the aims of the course are to Equip you with the application of statistical methods to the measurement and critical assessment of assumed economic relationships using data. Provide an improvedintroductory understanding of how the economy works, at either the microeconomic or macroeconomic level. INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 4 C ourse Objectives To achieve the aims mentioned above alsoto the overall stated course objectives. Each unit, in the beginning, has its specific objectives. You should read them before you start working through the unit. You may want to refer to them during your study of the unit to check on your progress and should always take a look back at the objectives after completion. In this way, you can be certain you have done what was necessary to you by the unit. The course objectives are set below for you to achieve the aims of the course. On successful conclusion of the course, you should be able to Know the basic principles of econometric analysis Express relationships between economic variables using mathematical concepts and theories Understand both the fundamental techniques and wide array of applications involving linear regression estimation Analyse the strengths and weaknesses of the basic regression model. Outline the assumptions of the normal linear regression model and discuss the significance of these assumptions Explain the method of ordinary least squares Test hypotheses of model parameters and joint hypotheses concerning more than one variable Discuss the consequences of multicollinearity, the procedures for identifying multicollinearity, and the techniques for dealing with it Explain what ismeant by heteroscedasticity, and the consequences for ordinary least square (OLS) estimators and prediction based on those estimators Assess the methods used to identify heteroscedasticity, including data plots and more formal tests, and the various techniques to deal with heteroscedasticity, including model transformations and estimation by weighted least squares Explain autocorrelation, and discuss the consequences of autocorrelated disturbances for the properties of OLS estimator and prediction based on those estimators Outline and discuss the methods used to identify autocorrelated disturbances, and what can be done about it, including estimation by generalised least squares Discuss the consequences of disturbance terms not being normally distributed, tests for non-normal disturbances, and methods to deal with non-normal disturbances, including the use of dummy variables Discuss the consequences of specifying equations incorrectly Discuss the tests used to identify correct model specification and statistical criteria for choosing between models Download 1.75 Mb. Share with your friends:
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