ECONS543-22A (HAM)

Applied Econometrics

15 Points

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Division of Management
School of Accounting, Finance and Economics

Staff

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Convenor(s)

Diem Vo

Diem Vo
MSB.2.01
See Moodle
diemthihong.vo@waikato.ac.nz

Frank Scrimgeour

Frank Scrimgeour
4415
MSB.2.03
See Moodle
frank.scrimgeour@waikato.ac.nz

Lecturer(s)

Diem Vo

Diem Vo
MSB.2.01
See Moodle
diemthihong.vo@waikato.ac.nz

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.
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    • For extensions starting with 3: dial +64 7 2620 + the last 3 digits of the extension e.g. 3123 = +64 7 262 0123.
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Paper Description

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This paper teaches basic skills in econometrics, which is the statistical analysis of economic data. Topics include but are not limited to, simple and multiple regression analysis, statistical inference, maximum likelihood estimation, generalized method of moments, limited dependent variables, time series analysis, and various technical problems that arise when applying econometric techniques to real-world data such as heteroskedasticity, multicollinearity, and endogeneity. The emphasis of the course will be on both theory and empirical applications. Throughout the Trimester, students will learn how to (i) develop a regression model, (ii) estimate it, and (iii) interpret it. General topics that we will cover include OLS regression, prediction, dummy variables, model specification, model selection, hypothesis testing, robust standard errors, endogeneity, qualitative choice models (logit and probit), Maximum Likelihood, Generalized Method of Moments, regression analysis with time-series data, and simulations. Students will gain experience estimating statistical models using programming language STATA and learn how to run Monte Carlo simulations to investigate the properties of estimators.

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Paper Structure

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This paper is taught through lectures and workshops in the laboratory. Both lectures and workshops will be recorded and uploaded on Moodle after the end of each session. Students will learn the background theory in lectures. In the workhops, students will be given a set of exercises to complete using STATA.

Students are expected to do the assigned readings before coming to class. Weekly workshops in the lab emphasize empirical applications using the statistical software package STATA. Students should be prepared to spend a considerable amount of time, outside of class time, to familiarise themselves with STATA coding.

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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • [1] Estimate relationships between variables using OLS regression; Interpret OLS regression output
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  • [2] 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
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  • [3] Test linear hypotheses about regression coefficients
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  • [4] Use regression output to predict values of the dependent variable for given values of the explanatory variables
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  • [5] Use modelling skills to develop, estimate, and analyse your own economic model
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  • [6] Understand (i) what serial correlation and heteroskedasticity are, (ii) their consequences for OLS regression, and (iii) how to estimate “robust” standard errors
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  • [7] Recognize applications where endogeneity is likely to be a problem, and understand its consequences for OLS regression
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  • [8] 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
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  • [9] Understand the consequences of using OLS to estimate regression models with a binary dependent variable
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  • [10] Learn how to run Monte Carlo simulations to investigate the properties of estimators
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  • [11] Learn how to use Stata programming language
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Assessment

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The course is 100% internally assessed. The assessment gives students a chance to demonstrate their competence in applied econometrics.

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Assessment Components

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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. Problem Set
3 Apr 2022
10:00 PM
10
  • Online: Submit through Moodle
2. Research Project
17 Apr 2022
11:30 PM
25
  • Online: Submit through Moodle
3. Mid-term Test
6 May 2022
2:00 PM
32
  • Other: In class and online zoom supervised off campus
✔
4. Final Test
27 Jun 2022
No set time
33
  • Other: In class and online zoom supervised off campus
✔
Assessment Total:     100    
Failing to complete a compulsory assessment component of a paper will result in an IC grade
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Required and Recommended Readings

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Required Readings

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There is no set text. Several books are recommended (and are held on course reserve in the library)

Highly recommended Jeffrey M. Wooldridge. 2019. Introductory Econometrics: A Modern Approach (7th Edition),

(Link of the book here: https://nz.cengage.com/c/isbn/9781337558860/).

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Recommended Readings

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James H. Stock and Mark W. Watson, 2011. Introduction to Econometrics

Jeffrey M. Wooldridge. 2015. Introductory Econometrics: A Modern Approach

Marno Verbeek. 2012. Guide to Modern Econometrics

Josh Angrist and J-S Pischke. 2015. Mastering 'metrics: The Path from Cause to Effect

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Other Resources

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In order for to you appreciate the ongoing development of econometric methods, and the debates over their various uses, it will be important to become familiar with some resources on the web (noting also that Stata is a web-aware software that can use the internet to facilitate collaboration and for the dissemination of results and of new estimation routines and other advances in the software).

Amongst the blogs that are worth sampling are:

Development Impact Blog

http://blogs.worldbank.org/impactevaluations/

Econometrics Beat:

http://davegiles.blogspot.co.nz/

A very useful resource for finding applied papers that use econometric methods is:

Research Papers in Economics

http://repec.org/

Resources on the internet for helping to learn Stata and for finding new estimation routines and other helpful additions to the core software are:

UCLA Resources to help you learn and use Stata

https://www.stata.com/links/resources-for-learning-stata/

Boston College Statistical Software Components

https://ideas.repec.org/s/boc/bocode.html

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Online Support

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All materials for a coming lecture and workshop will be available before class. Lecture notes will be provided in Moodle, after class.
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Workload

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Contact hours are typically four hours per week. An additional 8-10 hours per week should be kept aside for reading and practising to familiarise with Stata software and Stata coding. It is expected to spend a total of 150 hours for the ECONS303/543, including 24 hours for lectures, 18 hours for workshops, 2 hours for a problem set, 2 hours for a term test and 12-hour prepare for term test, 20 hour-prepare for the final exam, 72 hours for the research project, reading the recommended books, practising Stata coding and doing homework.
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Linkages to Other Papers

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Note any linkages to other papers where the linkage is of importance.
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Prerequisite(s)

Prerequisite papers: ECON204 or ECONS205 or equivalent and at least 30 points at 300 level or above in Economics and/or Finance, or if a student has gained A- or better in both ECONS205 or ECON204 and ECONS202 or ECON302

Corequisite(s)

Equivalent(s)

Restriction(s)

Restricted papers: ECON304, ECON404, ECON543

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