ECONS303-23A (HAM)

Applied Quantitative Research Methods

15 Points

Edit Header Content
Division of Management
School of Accounting, Finance and Economics

Staff

Edit Staff Content

Convenor(s)

John Gibson

John Gibson
4289
MSB.2.08
See Moodle
john.gibson@waikato.ac.nz

Lecturer(s)

Administrator(s)

: denise.martin@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

Student Representative(s)

Lab Technician(s)

Librarian(s)

: yilan.chen@waikato.ac.nz

You can contact staff by:

  • Calling +64 7 838 4466 select option 1, then enter the extension.
  • Extensions starting with 4, 5, 9 or 3 can also be direct dialled:
    • For extensions starting with 4: dial +64 7 838 extension.
    • For extensions starting with 5: dial +64 7 858 extension.
    • For extensions starting with 9: dial +64 7 837 extension.
    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
Edit Staff Content

What this paper is about

Edit What this paper is about Content
This paper provides the basic applied econometrics skills needed by students pursuing careers as professional economists, as policy analysts, and in the financial sector. The most common statistical methods of estimation and inference used in applied econometrics are covered. It is taught using a combination of lectures, workshops and laboratories and entails a considerable amount of independent research. The paper requires using two statistical software packages - R and Stata - and a set of 17 customized videos have been provided on these packages which are tailored to this particular paper. It will be the responsiblity of students to acquire the necessary level of computer skills to complete the paper. Knowledge of the material covered in ECONS205 (BUSAN205) and a working familiarity with basic calculus, matrix algebra and statistics is assumed, although the important relevant material is reviewed in the first week.
Edit What this paper is about Content

How this paper will be taught

Edit How this paper will be taught Content
The paper is taught through lectures, interactive workshops and laboratories. It includes self-paced video modules on R and Stata and a self-directed component in the form of an applied econometric research paper that students will prepare in small teams.
Edit How this paper will be taught Content

Required Readings

Edit Required Readings Content

There is no set text, but several books are recommended (and are held on course reserve)

Jeffrey M. Wooldridge. 2020. Introductory Econometrics: A Modern Approach (7th Edition)

James Stock and Mark Watson. 2020. Introduction to Econometrics (4th Edition)

Marno Verbeek. 2012. Guide to Modern Econometrics

James LeSage and Kelley Pace. 2009. Introduction to Spatial Econometrics

Edit Required Readings Content

Learning Outcomes

Edit Learning Outcomes Content

Students who successfully complete the course should be able to:

  • Define and explain commonly employed econometric terms
    Linked to the following assessments:
  • Specify and estimate econometric models from economic data in their cross-section, panel and time series forms
    Linked to the following assessments:
  • Critically appraise the research design of applied econometric studies
    Linked to the following assessments:
  • Interpret threats to the validity of reported econometric results
    Linked to the following assessments:
  • Interpret the output from econometric software
    Linked to the following assessments:
  • Use a statistical program (Stata or R) to estimate econometric models, test hypotheses, and undertake empirical exercises
    Linked to the following assessments:
  • Estimate relationships between variables using OLS regression; Interpret OLS regression output
    Linked to the following assessments:
  • Identify good instrumental variables and use 2SLS to correct for endogeneity bias; Critically analyse the results of 2SLS estimation to determine whether 2SLS represents an improvement over OLS
    Linked to the following assessments:
  • Interpret coefficient estimates in linear regression models, including coefficients for dummy variables, interaction effects, and quadratic terms; in both linear, logged and semi-logged specifications
    Linked to the following assessments:
  • Learn how to run Monte Carlo simulations to investigate the properties of estimators
    Linked to the following assessments:
  • Learn how to use Stata programming language
    Linked to the following assessments:
  • Recognize applications where endogeneity is likely to be a problem, and understand its consequences for OLS regression
    Linked to the following assessments:
  • Test linear hypotheses about regression coefficients
    Linked to the following assessments:
  • Understand (i) what serial correlation and heteroskedasticity are, (ii) their consequences for OLS regression, and (iii) how to estimate “robust” standard errors
    Linked to the following assessments:
  • Understand the consequences of using OLS to estimate regression models with a binary dependent variable
    Linked to the following assessments:
  • Use modelling skills to develop, estimate, and analyse your own economic model
    Linked to the following assessments:
  • Use regression output to predict values of the dependent variable for given values of the explanatory variables
    Linked to the following assessments:
Edit Learning Outcomes Content
Edit Learning Outcomes Content

Assessments

Edit Assessments Content

How you will be assessed

Edit How you will be assessed Content
The course is 100% internally assessed. A research paper is due during the examination period. The assessment gives students a chance to demonstrate their competence in applied econometrics.
Edit How you will be assessed Content

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0. There is no final exam. The final exam makes up 0% of the overall mark.

The internal assessment/exam ratio (as stated in the University Calendar) is 100:0 or 0:0, whichever is more favourable for the student. The final exam makes up either 0% or 0% of the overall mark.

Component DescriptionDue Date TimePercentage of overall markSubmission MethodCompulsory
1. Diagnostic Quiz
10 Mar 2023
4:00 PM
5
  • Hand-in: In Lecture
2. Midterm Test
6 Apr 2023
3:00 PM
30
  • Hand-in: In Lecture
3. Software competency test
12 May 2023
2:00 PM
20
  • Online: Submit through Moodle
✔
4. Oral presentation of motivation and research design for research paper
11 May 2023
5:00 PM
10
  • In Class: In Lecture
✔
5. Draft of Data Sources and Code Appendix
26 May 2023
11:00 PM
15
  • Email: Lecturer
✔
6. Research Paper
16 Jun 2023
5:00 PM
20
  • Online: Submit through Moodle
✔
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
Edit Assessments Content