Department of Economics

EC4044 Applied Economic Analysis - Autumn Semester 2016/17

Lecturer: / Teaching Assistant:
Dr. Stephen Kinsella / Mr. Niall Devitt
KB3-42 / KB3-39

Office hours Thursdays 10-12 /
Office Hours Wednesdays 10-12
ECTS Credits: 6 / Recommended Study: 7hrs per week

The objective of this module is to deepen and broaden students' knowledge from the intermediate micro and macroeconomics learned in EC4004, Economics for Business. This module will be taught using real world case studies, showing how the theory applies to a range of economic situations. Students will also be equipped with mathematical and statistical tools through the lecture and lab sessions, and bring data to the theory in written work, as well as becoming articulate in these matters through written submissions on topics they choose to write about, which will encourage proactivity and innovative behaviour.

Prerequisite Modules:EC4101, EC4102, EC4004

Resources (Texts)

- David Freedman, Statistical Models, Theory and Practice, Cambridge University Press, 2009. This is the best book on statistics I have ever read.

- Garrett Grolemund and Hadley Wickham, R for Data Science, O'Reilly, 2016. The standard reference work for data science using R.

- Gary Koop, Analysis of Economic Data, Wiley, 2013. This book is a classic and fun to read.

- Lots of online resources with R, especially slides, and further readings will be available on sulis.ul.ie before lectures. Podcasts of the lectures will be available.

Resources (Software)

- R and Rstudio.

- Github, where all the notes, code, and other elements for the course will be.

- Datacamp.com, for the introductions to R.

- SULIS contains the readings.

- Turnitin, for the final data project.

Graduate Attributes. UL’s graduate attributes are very clear. We work towards all of our graduates being knowledgeable, proactive, creative, responsible, collaborative, and articulate. This module works towards making undergraduates knowledgeable in both microeconomics and macroeconomics, responsible in their use and interpretation of data, collaborative through the problem solvingset we use, and articulate through the report-writing and in class discussions we will enter into, in particular.

Learning Outcomes:Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

  1. Knowledge: understanding intermediate modeling and economic reasoning of applied topics.
  2. Application of these theories to real world data.
  3. Evaluation of real world data using tools taught in lectures and lab
  4. Identification and analysis of a problem within the framework of economic models.
  5. Evaluate, critique and formulate solutions to an identified economic problem.
  6. Applying tools of dynamic analysis in research.

Assessment

- 2 Datacamp courses, 'introducing you to R', worth 5%, due Monday of week 3, and 'correlation and regression' in R, worth 10%, due Monday, of week 6.

- 1 optionalDatacamp course, which you choose, worth 10%. Peruse the course catalogue and let us know which one you want. You set the deadline and guide your own learning. You have access to the entire suite of modules for the entire semester. This would cost you 30 euros every month to learn, and you can add the certifications to you LinkedIn profiles, etc., for signaling purposes.

- 1 end of term project, due week 13, with a 1 page outline of your project agreed with Niall by week 8. Details of this will be given in the tutorials, it is worth 75%-85%, depending.

The objective is not to over-assess you. Rather, there are some basics you need to know to progress in this module, and then we’ll give you the space to let you play with the data and the tools we give you for 6-8 weeks. The more you use R for applied economic analysis, the better you'll be at it when it comes time to submit the applied economic analysis project.

Learning outcome(s) addressed / % of total grade / Week due
Datacamp courses online / 1, 2, 3, 5. / 15/25 / Weeks3, 6
Applied Economic Analysis Project / 1, 2, 3, 4, 5, 6. / 75/85 / Week13

Contact hours & Feedback. We have 12 lectures in the CSG001meeting Thursdays 4-6. Check timetable.ul.ie for your tutorial slot. Weekly compulsory tutorials begin in week 3. You can expect verbal and written feedback through Turnitin and during office hours for your project.

Lecture by lecture breakdown

Week 1 / Motivation, statistical basics and data handling
Reading: Koop, Chapter 2, Freedman, Chapter 1
Application: Growth Accounting & the Solow Model
Week 2 / Modelling using simple regression
Reading: Freedman Chapter 1, Koop Chapter 4.
Application: Wage/Salary Data
Week 3 / Tutorials Begin, Introduction to R module completed.
More on Simple Regression
Reading: Koop, Chapter 4, Freedman Chapter 3
Application: consumer theory revisited
Week 4 / Multiple regression
Reading: Freedman, Chapter 5
Application: education spending and educational attainment
Application: producer theory revisited.
Week 5 / Multiple regression 2
Reading: Freedman, Chapter 5, Koop Chapter 6
Application: Gender pay disparities
Application: Game theory, simulated.
Week 6 / Correlation and Regression in R module completed.
Time series analysis
Reading: Koop Chapter 9
Application: The Ramsey problem: the permanent income hypothesis funding pension systems in aging societies
Application: Volatility in asset prices
Application: (gapminder): How does life expectancy change over time for each country?
Week 7 / Machine learning 1
Reading: Varian, 2014.
Application: what is happening to working class white communities in the USA?
Week 8 / Machine learning 2
Reading: Athey, 2016
Application: Predicting the present: open economies and monetary unions
Application: Asymmetric information in markets
Week 9 / Big data and public policy
Reading: Manski, 2015
Application: uncertainty in policy applications
Week 10 / Interviewing & qualitative analysis 1
Reading: Truman Bewley, Why Wages don’t fall during a recession
Application: Interview yourselves
Week 11 / Interviewing & qualitative analysis 2
Reading: Burnard et al, Coding and Thematic Analysis, Nature.
Week 12 / Recap
Week 13 / Economic Analysis Project Due

KBS SCHEME OF GRADE DESCRIPTIONS

Grade / Award level / QPV / Description
A1 / First / 4.00 /
  • Outstanding performance
  • In-depth knowledge and understanding of principles and concepts related to the topic. Integrates information into a wider context.
  • Excellent analysis and interpretation.
  • Evidence of a significant amount of outside reading.
  • A logically structured and clear approach.
  • Answer is original and reflective.

A2 / First / 3.60 /
  • Excellent performance.
  • A comprehensive knowledge and understanding of principles and concepts.
  • Excellent analysis and interpretation.
  • Evidence of a significant amount of outside reading.
  • Answer may have neglected to deal with one or two minor aspects of the issues involved.
  • A logically structured and clear approach.

B1 / 2.1 / 3.20 /
  • Very good performance
  • A substantial but not totally comprehensive knowledge and understanding of principles and concepts.
  • Shows a very good competence in the subject without being outstanding.
  • Very good analysis and interpretation.
  • Some gaps in knowledge. Student can argue the key issues in an intellectually organised manner.
  • A logically structured and clear approach.

B2 / 2.1 / 3.00 /
  • Good performance
  • A competent and organised approach to the subject matter.
  • A reasonable knowledge and understanding of principles and concepts.
  • Very good analysis and interpretation.
  • Student is very familiar with the material covered in lecture notes, but may show limited evidence of wider reading.
  • Answers may be organised rather than inspired.

B3 / 2.2 / 2.80 /
  • Competent performance
  • Shows evidence of having put significant work into studying the subject.
  • A reasonable level of knowledge.
  • Good analysis and interpretation.
  • Some gaps/oversights in either knowledge, or in the approach taken. Limited evidence of wider reading.
  • Reasonable analytical and interpretative skills.
  • The work is still of sufficient standard to merit an honours award.

C1 / 2.2 / 2.60 /
  • Satisfactory performance
  • Shows a familiarity with the subject material covered in the question.
  • The approach taken to answering the question is rather limited
  • Focuses on material covered in lecture notes. Little or no evidence of wider reading.
  • A basic knowledge of key principles and concepts only.
  • Limited analytical and interpretative skills.

C2 / Third class honours / 2.40 /
  • Acceptable performance
  • Conversant with the subject area.
  • A good average answer, which does not stray beyond the basics.
  • Some significant gaps in knowledge.
  • Limited analytical and interpretative skills.

C3 / Third class honours / 2.00 /
  • Minimally acceptable performance
  • A basic pass. Shows a basic knowledge of key principles and concepts.
  • Significant gaps in knowledge or understanding.
  • May have omitted to answer part of the question.
  • Answer is basic and factual with some errors.
  • The standard of work is sufficient to obtain a passing grade.
  • Limited analytical and interpretative skills.

D1 / Compensating
Fail / 1.60 /
  • Weak performance, compensating fail
  • A poor answer, unsatisfactory in some significant ways.
  • Student is unable to correctly recall important material related to the question at hand.
  • Little evidence of analytical and interpretative skills.
  • Answer is disorganised and lacks intellectual depth.

D2 / Compensating fail / 1.20 /
  • Poor performance, compensating fail
  • Very poor answer. The student either has very little knowledge of the subject area, or lacks the ability to express their knowledge in an organised fashion.
  • Student may have shown some small knowledge of the area.
  • Little evidence of analytical and interpretative skills.

F / Fail / 0.00 /
  • An outright fail no compensation allowed.
  • The work is completely unsatisfactory and shows very little evidence of effort.
  • Little or no evidence of knowledge of key principles and concepts.
  • No evidence of analytical or interpretative skills.

For essay-type assignments, which are completed over a greater period of time than examinations, faculty assessing the work may also wish to give due regard to the following criteria:

  • Originality
  • Adoption of a critical perspective
  • Fulfilment of the initial brief
  • Referencing
  • Relevance to the topic
  • Factual accuracy
  • Grammar and spelling
  • Presentation

Guideline percentage bands associated each grade:

Grade

/ QPV / Percentage band
A1 / 4.00 / 75% or more
A2 / 3.60 / 70%
B1 / 3.20 / 65%
B2 / 3.00 / 60%
B3 / 2.80 / 55%
C1 / 2.60 / 50%
C2 / 2.40 / 45%
C3 / 2.00 / 40%
D1 / 1.60 / 35%
D2 / 1.20 / 30%
F / 0.00 / Less than 30%

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