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ECO453Social Sciences2 Unitsintermediate

Applied Econometrics

This course, Applied Econometrics I, provides undergraduate Economics students with a detailed understanding of econometric theory and its applications in data analysis for policy interpretations. It covers essential topics such as time series components, simple linear regression, multicollinearity, autoregressive processes, stationarity, cointegration analysis, and panel data regression models. Students will gain practical skills in model estimation using real-life data and econometric software, enabling them to evaluate and discuss econometric literature effectively.

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156h
Study Time
13
Weeks
12h
Per Week
intermediate
Math Level
Course Keywords
EconometricsRegressionTime SeriesPanel DataStationarity

Course Overview

Everything you need to know about this course

Course Difficulty

Intermediate Level
Builds on foundational knowledge
65%
intermediate
📊
Math Level
Moderate Math
🔬
Learning Type
Hands-on Practice

Course Topics

Key areas covered in this course

1

Applied Econometric Research

2

Time Series Analysis

3

Linear Regression

4

Autoregressive Models

5

Panel Data Models

6

Cointegration

Total Topics6 topics

Ready to Start

No specific requirements needed

This course is designed to be accessible to all students. You can start immediately without any prior knowledge or specific preparation.

Assessment Methods

How your progress will be evaluated (3 methods)

assignments

Comprehensive evaluation of course material understanding

Written Assessment

tutor-marked assignments

Comprehensive evaluation of course material understanding

Written Assessment

final examination

Comprehensive evaluation of course material understanding

Written Assessment

Career Opportunities

Explore the career paths this course opens up for you

Economist

Apply your skills in this growing field

Data Analyst

Apply your skills in this growing field

Financial Analyst

Apply your skills in this growing field

Policy Analyst

Apply your skills in this growing field

Researcher

Apply your skills in this growing field

Industry Applications

Real-world sectors where you can apply your knowledge

FinanceEconomicsGovernmentConsultingResearch

Study Schedule Beta

A structured 13-week journey through the course content

Week
1

MODULE 1 INTRODUCTION TO ECONOMETRIC RESEARCH USING SOFTWARE

3h

Unit 1: Meaning of Applied Econometric Research

3 study hours
  • •Understand the definition of econometrics and its applications
  • •Explore the basic tools used in econometric analysis
  • •Learn the stages of applied econometric research
  • •Discuss the assumptions underlying econometric models
  • •Critique econometric research methods
Week
2

MODULE 1 INTRODUCTION TO ECONOMETRIC RESEARCH USING SOFTWARE

3h

Unit 2: Time Series and Its Components

3 study hours
  • •Define time series data and its components
  • •Understand secular trends, seasonal variations, cyclical variations, and irregular variations
  • •Learn methods for estimating trends, including free hand, regression, and moving average methods
  • •Graphically represent and estimate trends
Week
3

MODULE 1 INTRODUCTION TO ECONOMETRIC RESEARCH USING SOFTWARE

3h

Unit 3: Simple Linear Regression Model

3 study hours
  • •Explain the linear regression approach
  • •Discuss parameter estimation procedures
  • •Evaluate the assumptions of stochastic variables
  • •Analyze the assumptions of explanatory variables
  • •Estimate simple regression using algebraic and software methods
Week
4

MODULE 1 INTRODUCTION TO ECONOMETRIC RESEARCH USING SOFTWARE

3h

Unit 4: Time Series Data Analysis

3 study hours
  • •Discuss the history of Time Series Data Analysis
  • •Explain the Stochastic Process
  • •Evaluate Stationary and Nonstationary Variables
  • •Determine weakly Stationarity and Strict Stationarity
  • •Run Time Series Data in Eviews 12
Week
5

MODULE 1 INTRODUCTION TO ECONOMETRIC RESEARCH USING SOFTWARE

3h

Unit 5: Multicollinearity

3 study hours
  • •Define multicollinearity
  • •Explain types of multicollinearity
  • •Discuss consequences of multicollinearity
  • •Highlight solutions to multicollinearity problems
Week
6

MODULE 2 STATIONARITY AND AUTOREGRESSIVE PROCESS

3h

Unit 1. Autoregressive Process

3 study hours
  • •Discuss the meaning of the term Autoregressive (AR)
  • •Explain estimation of an Autoregressive Model (AR)
  • •State autocorrelation or Serial Correlation
  • •Analyze consequences of Serial Correlation
  • •Carry out the LM Test
Week
7

MODULE 2 STATIONARITY AND AUTOREGRESSIVE PROCESS

3h

Unit 2: Concept of Stationarity

3 study hours
  • •Discuss stationarity and non-stationarity
  • •Explain the meaning of unit root and its importance
  • •Explain how to stationarise non-stationary series
  • •Conduct unit roots test in the AR(1) Model
  • •Estimate stationarity of variables in EViews software
Week
8

MODULE 2 STATIONARITY AND AUTOREGRESSIVE PROCESS

3h

Unit 3: Cointegration Analysis

3 study hours
  • •Discuss the meaning of cointegration
  • •Learn how to conduct cointegration Test
  • •Discuss the types of cointegration tests
  • •Conduct cointegration test using EViews
Week
9

MODULE 2 STATIONARITY AND AUTOREGRESSIVE PROCESS

3h

Unit 4: Autoregressive Distributed Lag (ARDL) Model

3 study hours
  • •Discuss ARDL Cointegration Equations
  • •Explain the justification for the choice of ARDL model
  • •Compute ARDL in Eviews
  • •Design the ARDL Bound cointegration test model
  • •Conduct a diagnostic check for Serial Correlation in ARDL
Week
10

MODULE 2 STATIONARITY AND AUTOREGRESSIVE PROCESS

3h

Unit 5: ARDL Post Estimation Tests

3 study hours
  • •Know why post estimation tests are carried
  • •List various types of post estimation tests required
  • •Interpret appropriate post estimation tests result
  • •Conduct Hypotheses Testing Using Wald-Test
Week
11

MODULE 3: PANEL DATA ESTIMATION

3h

Unit 1: Panel Data Regression Model

3 study hours
  • •Know the meaning of Panel Data Regression Model
  • •Explain panel Data Examples
  • •Discuss the advantages of Panel Data
  • •List the importance of Panel Data
  • •Design the format of a Panel Data
  • •List types of Panel Data
Week
12

MODULE 3: PANEL DATA ESTIMATION

3h

Unit 2: Fixed Versus Random Effects Panel Data

3 study hours
  • •Discuss fixed versus random effects model
  • •Explain the advantages of fixed effect model
  • •Discuss the advantages of random effect model
  • •Explain the difference between fixed and random effects model
Week
13

MODULE 3: PANEL DATA ESTIMATION

3h

Unit 3: Testing Fixed and Random Effects

3 study hours
  • •Test fixed and random effects
  • •Discuss Breusch-Pagan LM Test for Random Effects
  • •Discuss Hausman Test for Comparing Fixed and Random Effects
  • •Understand guidelines of Model Selection

This study schedule is in beta and may not be accurate. Please use it as a guide and consult the course outline for the most accurate information.

Course PDF Material

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Study Tips & Exam Preparation

Expert tips to help you succeed in this course

1

Review all units, focusing on key concepts and formulas

2

Practice with EViews software to estimate models

3

Solve past examination questions to understand question patterns

4

Create concept maps linking different econometric models

5

Focus on understanding assumptions and limitations of each model

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