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EC203: Applied Econometrics

• Thomas Martin

Module Leader
30 CATS - Department of Economics
Summer Module
Spring Module
Autumn Module

Principal Aims

This module allows students to develop an understanding of fundamental and intermediate concepts of statistical analysis, such as regression analysis. Students will also develop the capacity to apply statistical techniques to real world problems/data sets using the statistical package STATA.

Principal Learning Outcomes

Subject Knowledge and Understanding:...demonstrate understanding of random variables, associated distributions and moments; statistical estimation, estimator sampling distributions and population inference; causality and selection bias; experimental versus non-experimental data; simple linear regression (SLR) model, assumptions, interpretation and hypothesis testing; multiple linear regression (MLR) model, assumptions, interpretation and hypothesis testing; modelling non-linear relationships; dummy variables; interaction terms; the failure of MLR assumptions; tests and implications for hypothesis testing; problems of endogeneity; instrumental variables; short panel data methods; Stata.

Knowledge and understanding of:... (i) Economic Principles; core concepts and methods in macroeconomics. (ii) An awareness of the empirical approach to economics and social science. (iii) Reviewing and extending fundamental statistical concepts, including principal component analysis. (iv) Regression analysis, its extensions and applications.

Syllabus

The module will typically cover the following topics:

Review of random variables, associated distributions and moments; review of statistical estimation, estimator sampling distributions and population inference; causality and selection bias; experimental versus non-experimental data; simple linear regression (SLR) model, assumptions, interpretation and hypothesis testing; multiple linear regression (MLR) model, assumptions, interpretation and hypothesis testing; modelling non-linear relationships; dummy variables; interaction terms; the failure of MLR assumptions; tests and implications for hypothesis testing; problems of endogeneity; instrumental variables; short panel data methods; Stata.

Context

Core Module
LM1D (LLD2) - Year 1, V7ML - Year 2
Optional Core Module
V7MR - Year 2, LM1D (LLD2) - Year 2
Optional Module
V7ML - Year 2, V7MP - Year 2, V7ML - Year 3, V7MP - Year 3
Pre or Co-requisites

EC120 for Economics students, including joint degrees (excluding GL11).

IB122 for WBS students.

EC106 or EC107 for all other students.

QS104 with a mark of 65% (for LA99 students only)

Summary:

Modules: EC106-24 or EC107-30 or (EC121-12 and EC122-12 and EC125-6) or (EC123-12 and EC124-12 and EC125-6)

Restrictions
May not be combined with modules
Part-year Availability for Visiting Students
Available in the Autumn term only (1 x test - 12 CATS) and in the Autumn and Spring terms together (2 x tests and 1 x assignment - 24 CATS) Monash = Available in the Autumn term only (1 x test & 1 assignment – 15 CATS)

Assessment

Assessment Method
Coursework (40%) + Online Examination (60%)
Coursework Details
Empirical Assignment (15%) , Online Examination (60%) , Test 1 (12%) , Test 2 (13%)
Exam Timing
Summer

Exam Rubric

Time Allowed: 3 hours plus 15 minutes reading time.

Read all instructions carefully- and read through the entire paper at least once before you start entering your answers.

There is ONE section in this paper. Answer ALL TEN questions (10 marks each).

Approved pocket calculators are allowed. Statistical Tables and a Formula Sheet are provided.

You should not submit answers to more than the required number of questions. If you do, we will mark the questions in the order that they appear, up to the required number of questions in each section.

Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.