ECO713

Applied Econometrics

Social Sciences
700 level
3 credit units
240 pages
10 units

This course on Applied Econometrics equips postgraduate Economics students with the skills to quantify economic relationships using statistical and mathematical tools. It covers stages of econometric research, regression analysis, econometric problems like heteroscedasticity and multicollinearity, identification issues, dummy variables, distributed lags, and simultaneous equation estimation methods. Students will learn to apply these techniques to analyze time series data and solve economic problems, enhancing their capabilities as quantitative economists.

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About this course

Difficulty: Intermediate
Study hours: 156 hours
Maths: Intermediate
Content: Theoretical, practical, problem solving
Practical work: Yes

How it is assessed

Assignments
Tutor Marked Assignments
Final Examination

What you'll read

The real module and unit structure of ECO713, taken from the course material NOUN publishes.

Module 1

Module 2 · ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS

Module 3 · MATRIX TREATMENT OF REGRESSION ANALYSIS AND TIME SERIES ECONOMETRICS

One paragraph, so you can see how it reads

ECO713 · Unit 4: Statistical Test of Significance for Simple and Multiple Regressions

The presentation schedule included in your course materials gives you the important dates for this year for the completion of tutor-marking assignments and attending tutorials. Remember, you are required to submit all your assignments by due date. You should guide against falling behind in your work.

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What you should be able to do

Define econometrics and its scope.
Apply regression analysis techniques.
Identify and address econometric problems.
Understand and solve identification issues.
Estimate simultaneous equation models.
Apply matrix algebra to econometric models.
Analyze time series data.
Test for unit roots and cointegration.
Interpret error correction models.

What it prepares you for

Careers

Economist
Financial Analyst
Market Researcher
Policy Analyst
Data Scientist

Where it is applied

Finance
Economics
Government
Consulting
Research

Where it gets hard

The units students slow down on, and what makes each one heavy.

Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
Unit 2: Basic Ideas of the Identification Problem, Dummy variables and Distributed lag Models
Understanding the implications of identification problems requires a solid grasp of economic theory and model specification.
Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
Unit 3: Simultaneous Equation Estimation Methods (2SLS, 3SLS, etc)
Simultaneous equation models require understanding of multiple equations and their interdependencies, making it difficult to grasp the underlying economic relationships.
MODULE THREE: MATRIX TREATMENT OF REGRESSION ANALYSIS, TIME SERIES ECONOMETRICS
Unit 1: Matrix Treatment of Multiple Regressions
Advanced matrix operations and their application to regression analysis require strong mathematical aptitude and can be challenging for students with limited math background.

A suggested way through it

Suggested

13 weeks, about 42 hours in total. Yours will differ.

Week 1Module 1: DEFINITON AND SCOPE OF ECONOMETRICS, REGRESSON ANALYSIS AND THE STATISTICAL TEST OF SIGNIFICANCE
- Unit 1: Definition and Scope of Econometrics · 3 hours
  Read the unit introduction and objectives.. Define econometrics and its relationship to economics, mathematics, and statistics.. Explain the scope and division of econometrics.. Discuss the goals and stages of econometric research.. Differentiate between economic and econometric models..
Week 2Module 1: DEFINITON AND SCOPE OF ECONOMETRICS, REGRESSON ANALYSIS AND THE STATISTICAL TEST OF SIGNIFICANCE
- Unit 2: Simple Regression Model · 3 hours
  Explain the meaning of simple regression model.. Describe the assumptions of the linear stochastic regression model.. Discuss the Least Squares Criterion and the Normal Equations of OLS.. Solve numerical problems related to simple regression..
Week 3Module 1: DEFINITON AND SCOPE OF ECONOMETRICS, REGRESSON ANALYSIS AND THE STATISTICAL TEST OF SIGNIFICANCE
- Unit 3: Multiple Regression Model · 3 hours
  Illustrate models with two explanatory variables.. Derive the normal equation of two explanatory variables.. Estimate the coefficient of multiple determinations and the adjusted coefficient of multiple determinations.. Solve numerical problems related to multiple regression..
Week 4Module 1: DEFINITON AND SCOPE OF ECONOMETRICS, REGRESSON ANALYSIS AND THE STATISTICAL TEST OF SIGNIFICANCE
- Unit 4: Statistical Test of Significance of Parameter Estimates · 3 hours
  Calculate the mean and variance of parameter estimates.. Test the statistical significance of the parameter estimates using t-tests and F-tests.. Construct confidence intervals for parameter estimates.. Interpret the results of statistical tests in the context of economic theory..
Week 5Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 1: Econometric Problems (Heteroscedasticity, Autocorrelation and Multicollinearity) · 3 hours
  Examine econometric problems of heteroscedasticity: their causes, detection, consequences and correction.. Use econometric software to detect and correct for heteroscedasticity in regression models.. Apply appropriate diagnostic tests for heteroscedasticity..
Week 6Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 1: Econometric Problems (Heteroscedasticity, Autocorrelation and Multicollinearity) · 3 hours
  Examine econometric problems of autocorrelation: their causes, detection, consequences and correction.. Use econometric software to detect and correct for autocorrelation in regression models.. Apply appropriate diagnostic tests for autocorrelation..
Week 7Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 1: Econometric Problems (Heteroscedasticity, Autocorrelation and Multicollinearity) · 3 hours
  Examine econometric problems of multicollinearity: their causes, detection, consequences and correction.. Use econometric software to detect and correct for multicollinearity in regression models.. Apply appropriate diagnostic tests for multicollinearity..
Week 8Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 2: Basic Ideas of the Identification Problem, Dummy variables and Distributed lag Models · 3 hours
  Define what identification problem is all about.. State the implications of identification problems.. State the formal rules or conditions for identification.. Apply order and rank conditions to determine identifiability of equations..
Week 9Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 2: Basic Ideas of the Identification Problem, Dummy variables and Distributed lag Models · 3 hours
  State the nature of dummy variables.. Compute ANOVA models.. Estimate ANCOVA models.. Analyse regression with a mixture of quantitative and qualitative regressors.. Examine the use of dummy variables in seasonal analysis.
Week 10Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 3: Simultaneous Equation Estimation Methods (2SLS, 3SLS, etc) · 3 hours
  State the nature of simultaneous-equation model.. Identify simultaneous-equation bias in a model and the inconsistency of the OLS estimators.. Describe approaches to simultaneous-equation estimators.. Examine recursive models and the OLS. Determine estimation of exactly identified and over-identified equations..
Week 11Module 2: ECONOMETRIC PROBLEMS, BASIC IDEAS OF THE IDENTIFICATION PROBLEM AND SIMULTENEOUS EQUATION ESTIMATION METHODS
- Unit 4: Matrix treatment of Multiple Regression and Advanced treatment of Simultaneous Equation Estimation Techniques. · 3 hours
  Analyse matrix formulation of the regression model. Estimate least squares estimate in matrix notation. Analyse further matrix result for multiple regression. Determine the method of Instrumental Variables (IV). Examine the method of Generalised Least Squares (GLS). Solve the method of Three Stage least Squares (3SLS).
Week 12MODULE THREE: MATRIX TREATMENT OF REGRESSION ANALYSIS, TIME SERIES ECONOMETRICS
- Unit 2: Vector Auto Regressive (VAR) Models · 3 hours
  Analyze matrix formulation of the regression model. Estimate least squares estimate in matrix notation. Analyze further matrix result for multiple regression. Differentiate between univariate and multivariate time series models.. Understand Vector Autoregressive (VAR) models and discuss their advantages.. Understand the concept of causality and its importance in economic applications..
Week 13MODULE THREE: MATRIX TREATMENT OF REGRESSION ANALYSIS, TIME SERIES ECONOMETRICS
- Unit 3: Non-Stationarity and Unit Roots · 3 hours
  Understand the concept of stationarity.. Understand the importance of stationarity and the concept of spurious regressions.. Understand the concept of unit roots in time series.. Estimate the DF, ADF and PP tests using appropriate software. Understand the concept of cointegration in time series..
- Unit 4: Cointegration and Error Correction Model · 3 hours
  Appreciate the importance of cointegration and long-run solutions in econometric applications.. Understand the error-correction mechanism and its advantages.. Test for cointegration using the Engle–Granger approach.. Test for cointegration using the Johansen approach.. Obtain results of cointegration tests error-correction models and using appropriate econometric software..

Preparing for the exam

What to do

Review definitions and scope of econometrics (Unit 1).
Practice simple and multiple regression problems (Units 2-3).
Understand causes, detection, and correction of econometric problems (Module 2).
Study identification rules and simultaneous equation methods (Module 2).
Master matrix algebra for regression analysis (Module 3, Unit 1).
Focus on time series concepts: stationarity, unit roots, cointegration (Module 3, Units 2-4).
Practice VAR model estimation and causality tests (Module 3, Unit 2).
Review exam-style questions in TMAs and self-assessment exercises.
Create concept maps linking key concepts from different modules.
Allocate study time proportionally based on unit weightings in course guide.

Questions students ask about this course

What is ECO713 about?

How many units does ECO713 have?

ECO713, Applied Econometrics, has 10 units across 3 modules, over 240 pages of course material. You can read it one unit at a time.

How many credit units is ECO713?

ECO713 carries 3 credit units, at 700 level in Social Sciences.

Is ECO713 hard?

ECO713 is rated intermediate level, with intermediate mathematical content. It is mostly theoretical, practical and problem solving work, and it has a practical component.

How long does ECO713 take to study?

About 156 hours of study, spread across its 10 units.

How is ECO713 assessed?

ECO713 is assessed by Assignments, Tutor Marked Assignments and Final Examination.

What can I do with ECO713?

Economist, Financial Analyst, Market Researcher, Policy Analyst and Data Scientist.