ECON2002 Intermediate Applied Econometrics
The Complete Study & Assessment Guide · S2 2026ECON2002 Overview
- Adelaide University School of Economics
- Semester 2, 2026
- an undergraduate level 2 course
- 6 units
ECON2002 Intermediate Applied Econometrics develops data description, simple and multiple regression, inference, qualitative regressors, heteroskedasticity and model specification using statistical software. It is taught within Adelaide University School of Economics. It is an undergraduate level 2 course. It carries 6 units.
- Four evidence modes The 20/15/15/50 split tests recognition, written reporting, oral defence and extended problem solving.
- Interpretation first Every coefficient needs units, a ceteris-paribus reading and a statement of the assumptions that make inference defensible.
- Offering boundary The public S2 page names a 50% problem-solving task. An LMS snapshot contains exam conditions for its captured offering, but live S2 instructions control the current format, duration and resources.
What you get in the full ECON2002 Study & Assessment Guide
- 9 chapters, 25 pages — every assessed topic
- 12 original practice questions, each with a model answer
- 9 worked examples, solved the way ECON2002 marks
- 17 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Problem-Solving Task (50%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How ECON2002 is assessed
| Component | Weight | Format |
|---|---|---|
| Multiple-Choice Components | 20% | Four multiple-choice components in total; live timing and conditions are not on the public page |
| Report | 15% | Written econometric report; current brief and deadline must be checked in the course site |
| Oral Defence | 15% | Oral communication and defence of method or result; current format must be checked live |
| Problem-Solving Task | 50% | The public page does not describe this item as an examination |
The current public page publishes four MCQ components worth 20% together, a 15% report, a 15% oral defence and a 50% problem-solving task. A captured LMS page describes secure-assessment conditions for its captured offering, but the public S2 2026 page neither labels the current 50% item an examination nor publishes its conditions. This guide does not transfer those older conditions to the current offering; confirm the live task brief.
What other students are asking about ECON2002
- Where can I find lecture notes or study guides for ECON2002 at Adelaide?
- The ECON2002 study guide in AskSia Library covers Intermediate Applied Econometrics in 9 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in ECON2002?
- No final exam is listed in the offering this AskSia Library guide is built from; ECON2002 is assessed by Multiple-Choice Components 20%, Report 15%, Oral Defence 15%, Problem-Solving Task 50%.
- What does ECON2002 cover on simple regression and the ols estimator?
- In AskSia Library's ECON2002 guide, the Simple Regression and the OLS Estimator chapter explains population regression function · ordinary least squares · fitted value and residual · estimate a simple relationship and decompose each observation into fitted value and residual.
- What does ECON2002 cover on sampling uncertainty and inference?
- In AskSia Library's ECON2002 guide, the Sampling Uncertainty and Inference chapter explains sampling variance · standard error · confidence interval · move from a fitted coefficient to an interval or test while keeping the sampling assumptions visible.
- What does ECON2002 cover on multiple regression and ceteris-paribus interpretation?
- In AskSia Library's ECON2002 guide, the Multiple Regression and Ceteris-Paribus Interpretation chapter explains multiple regression · ceteris-paribus coefficient · omitted-variable bias · interpret a partial coefficient and explain what is and is not held constant in the comparison.
Current ECON2002 dates
| Date | Item | Control |
|---|---|---|
| 21 August 2026 | Semester 2 enrol-by date | Administrative enrolment date, not an assessment deadline. |
| 4 September 2026 | Semester 2 census date | Confirm financial and enrolment implications on the official page. |
| 18 September 2026 | Semester 2 last day to withdraw without fail | Assessment due dates are not published on the captured course page. |
Dates are as published in the current Adelaide University public course page. Confirm exact deadlines and submission settings in the live LMS.
What ECON2002 covers
Build the course in three arcs: Economic Data and Visual Evidence establishes the frame, Qualitative Regressors and Group Comparisons deepens it, and Econometric Report, Oral Defence and Problem Solving tests the complete method.
Economic Data and Visual Evidence
cross-sectional data · time-series data · data visualisation · classify an economic data set and choose a visual that reveals structure relevant to modellingDefine cross-sectional data
The captured teaching materials give this chapter a concrete anchor: The notes distinguish cross-section, time series, pooled cross-sections and panel data through wage, growth, minimum-wage and city-crime examples, warning that the wrong data structure can make a method misleading.
- cross-sectional data
- time-series data
- data visualisation
- Applying cross-sectional data
- Limits of time-series data and data visualisation
Simple Regression and the OLS Estimator
population regression function · ordinary least squares · fitted value and residual · estimate a simple relationship and decompose each observation into fitted value and residualDefine population regression function
The captured teaching materials give this chapter a concrete anchor: The verified course material separate population and sample regression, define the least-squares criterion, and use Workshop distinctions between an unobserved error and a fitted residual to control interpretation.
- population regression function
- ordinary least squares
- fitted value and residual
- Applying population regression function
- Limits of ordinary least squares and fitted value and residual
Sampling Uncertainty and Inference
sampling variance · standard error · confidence interval · move from a fitted coefficient to an interval or test while keeping the sampling assumptions visibleDefine sampling variance
The captured teaching materials give this chapter a concrete anchor: The Workshop set moves between p-values, confidence intervals and coefficient tests in settings including physical activity and BMI, renewable energy and GDP, and microfinance and women's income.
- sampling variance
- standard error
- confidence interval
- Applying sampling variance
- Limits of standard error and confidence interval
Multiple Regression and Ceteris-Paribus Interpretation
multiple regression · ceteris-paribus coefficient · omitted-variable bias · interpret a partial coefficient and explain what is and is not held constant in the comparisonDefine multiple regression
The captured teaching materials give this chapter a concrete anchor: The notes use education returns, police and crime, and minimum wages to show why regression is not automatically causal; Workshop applications include hiring, movie profit, wine aging and house characteristics.
- multiple regression
- ceteris-paribus coefficient
- omitted-variable bias
- Applying multiple regression
- Limits of ceteris-paribus coefficient and omitted-variable bias
Qualitative Regressors and Group Comparisons
dummy variable · reference category · interaction term · encode categories, choose a reference group and test whether slopes or intercepts differ meaningfullyDefine dummy variable
The captured teaching materials give this chapter a concrete anchor: The indicator-variable notes model group intercepts and education-by-gender slope differences, while Workshop 8 connects interactions and multicollinearity to house-price predictors such as bedrooms and bathrooms.
- dummy variable
- reference category
- interaction term
- Applying dummy variable
- Limits of reference category and interaction term
Functional Form and Logarithmic Models
functional form · logarithmic transformation · marginal effect · select a level or logarithmic specification and translate its coefficient using the correct unitsDefine functional form
The captured teaching materials give this chapter a concrete anchor: The materials contrast quadratic, log-log and log-level specifications and use a digital-advertising and sales example to interpret proportional effects and diminishing returns alongside fit measures.
- functional form
- logarithmic transformation
- marginal effect
- Applying functional form
- Limits of logarithmic transformation and marginal effect
Heteroskedasticity and Robust Inference
heteroskedasticity · robust standard error · residual diagnostic · detect unequal error variance and explain what robust inference corrects and what it leaves unresolvedDefine heteroskedasticity
The captured teaching materials give this chapter a concrete anchor: The correction notes distinguish heteroskedasticity-robust standard errors from GLS, WLS and feasible GLS; Workshop 9 uses applied residual patterns and beer-consumption inference to test what each remedy changes.
- heteroskedasticity
- robust standard error
- residual diagnostic
- Applying heteroskedasticity
- Limits of robust standard error and residual diagnostic
Model Specification and Statistical Validity
model specification · specification error · statistical validity · compare plausible specifications and use theory, diagnostics and sensitivity to justify a preferred modelDefine model specification
The captured teaching materials give this chapter a concrete anchor: The F-test materials formulate joint coefficient restrictions and compare restricted with unrestricted models rather than treating a collection of individual t tests as the same question.
- model specification
- specification error
- statistical validity
- Applying model specification
- Limits of specification error and statistical validity
Econometric Report, Oral Defence and Problem Solving
reproducible analysis · result-to-claim chain · oral defence · turn software output into a written and spoken argument that can survive specification questionsDefine reproducible analysis
The captured teaching materials give this chapter a concrete anchor: The captured LMS snapshot describes a two-hour face-to-face, closed-book exam with one A4 aid for its captured offering, but the current S2 2026 public page labels the 50% item a problem-solving task and publishes no conditions.
- reproducible analysis
- result-to-claim chain
- oral defence
- Applying reproducible analysis
- Limits of result-to-claim chain and oral defence
It is positioned as a university-wide elective and an included course in the Bachelor of Economics.
The current Adelaide University page couples econometric method with interpretation and communication. Its assessment design separates repeated multiple-choice checks, a report, an oral defence and a large problem-solving task.
Captured notes, Workshops and solutions add course-specific teaching evidence without being allowed to override the current public assessment labels.
Assessment in ECON2002 is distributed as follows: four multiple-choice components worth 20% in total, a 15% report, a 15% oral defence, and a 50% problem-solving task
The operational assessment conditions matter here.
The current S2 2026 public page labels the 50% component a problem-solving task and does not publish duration, permitted resources or task dates. A captured LMS exam-information page describes a two-hour, face-to-face, closed-book exam with one A4 aid for its captured offering; those conditions are retained as historical study evidence and are not transferred to the current offering.
Check the live task brief.
What makes ECON2002 demanding is concrete: Separating what OLS calculates from what an economic interpretation can claim: students must identify the data structure, justify specification and inference assumptions, diagnose violations and communicate how the result changes the substantive question.
No component-level hurdle is published on the captured official course page.
This is source silence, and a live course-site instruction may still add an operational requirement.
For enrolment planning, Students must have completed ECON1000 Principles of Economics and ECON1012 Data Analytics; ECON2515 is an antirequisite.
Build the course in three arcs: Economic Data and Visual Evidence establishes the frame, Qualitative Regressors and Group Comparisons deepens it, and Econometric Report, Oral Defence and Problem Solving tests the complete method.
Coverage note: ECON2002 is the current Adelaide University code and ECON2515 is its antirequisite; this guide does not substitute the separate ECON2006 course found in some new program structures.
Worked example · free
Interpret a log-wage coefficient and test the specification boundary
- 1Identify log wage as the dependent variable and education as the focal explanatory variable.
- 1Translate 0.072 into an approximate 7.2% conditional wage difference for one additional year of education.
- 1Hold included experience constant and avoid describing the coefficient as an unconditional difference.
- 1Name a plausible omitted factor, such as ability or local labour-market access, that can undermine a causal reading.
Key terms
- Ordinary least squares
- An estimation method choosing coefficients that minimise the sum of squared sample residuals.
- Heteroskedasticity
- A condition in which the regression error variance differs across observations or explanatory-variable values.
- Model specification
- The selection of variables, transformations and structural relationships included in an empirical model.
- Dependent variable
- The outcome variable whose conditional behaviour a regression model is designed to explain or predict.
- Explanatory variable
- A measured variable included in a model to account for variation in the dependent variable.
- Regression coefficient
- A model parameter describing the conditional change in an outcome associated with an explanatory variable.
- Statistical inference
- The use of sample evidence and a probability model to make qualified claims about population parameters.
- Dummy variable
- A numerical indicator, commonly coded zero and one, representing membership in a qualitative category.
- Functional form
- The mathematical shape chosen to relate the dependent variable to explanatory variables in a model.
ECON2002 FAQ
How is ECON2002 assessed?
four multiple-choice components worth 20% in total, a 15% report, a 15% oral defence, and a 50% problem-solving task
Where do students usually lose marks in ECON2002?
Separating what OLS calculates from what an economic interpretation can claim: students must identify the data structure, justify specification and inference assumptions, diagnose violations and communicate how the result changes the substantive question.
Which current ECON2002 dates are captured?
Semester 2 enrol-by date: 21 August 2026; Semester 2 census date: 4 September 2026; Semester 2 last day to withdraw without fail: 18 September 2026. Confirm any change and the exact submission setting in the live LMS.
What is the ECON2002 final assessed-task format?
The current S2 2026 public page labels the 50% component a problem-solving task and does not publish duration, permitted resources or task dates. A captured LMS exam-information page describes a two-hour, face-to-face, closed-book exam with one A4 aid for its captured offering; those conditions are retained as historical study evidence and are not transferred to the current offering. Check the live task brief.
What prerequisites or restrictions apply to ECON2002?
Students must have completed ECON1000 Principles of Economics and ECON1012 Data Analytics; ECON2515 is an antirequisite.
Does ECON2002 have a hurdle or component-level pass rule?
No component-level hurdle is published on the captured official course page. This is source silence, and a live course-site instruction may still add an operational requirement.
Is this ECON2002 resource an official university guide?
No. It is an independent ECON2002 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each ECON2002 chapter
- What is the main task in Economic Data and Visual Evidence?
- Classify an economic data set and choose a visual that reveals structure relevant to modelling. Chapter 01 · Economic Data and Visual Evidence
- What is the main task in Simple Regression and the OLS Estimator?
- Estimate a simple relationship and decompose each observation into fitted value and residual. Chapter 02 · Simple Regression and the OLS Estimator
- What is the main task in Sampling Uncertainty and Inference?
- Move from a fitted coefficient to an interval or test while keeping the sampling assumptions visible. Chapter 03 · Sampling Uncertainty and Inference
- What is the main task in Multiple Regression and Ceteris-Paribus Interpretation?
- Interpret a partial coefficient and explain what is and is not held constant in the comparison. Chapter 04 · Multiple Regression and Ceteris-Paribus Interpretation
- What is the main task in Qualitative Regressors and Group Comparisons?
- Encode categories, choose a reference group and test whether slopes or intercepts differ meaningfully. Chapter 05 · Qualitative Regressors and Group Comparisons
- What is the main task in Functional Form and Logarithmic Models?
- Select a level or logarithmic specification and translate its coefficient using the correct units. Chapter 06 · Functional Form and Logarithmic Models
- What is the main task in Heteroskedasticity and Robust Inference?
- Detect unequal error variance and explain what robust inference corrects and what it leaves unresolved. Chapter 07 · Heteroskedasticity and Robust Inference
- What is the main task in Model Specification and Statistical Validity?
- Compare plausible specifications and use theory, diagnostics and sensitivity to justify a preferred model. Chapter 08 · Model Specification and Statistical Validity
- What is the main task in Econometric Report, Oral Defence and Problem Solving?
- Turn software output into a written and spoken argument that can survive specification questions. Chapter 09 · Econometric Report, Oral Defence and Problem Solving
How to prepare for the assessments
Retrieve the course map, practise the recurring method—define the economic question and data structure, specify and estimate a regression, diagnose the assumptions and translate the statistical output into a defensible substantive claim—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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