ECONS507-21G (HAM)

Quantitative Skills for Finance and Economics

15 Points

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Division of Management
School of Accounting, Finance and Economics

Staff

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Convenor(s)

Lecturer(s)

Administrator(s)

: denise.martin@waikato.ac.nz

Placement/WIL Coordinator(s)

Tutor(s)

: rebekah.terito@waikato.ac.nz

Student Representative(s)

Lab Technician(s)

Librarian(s)

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.
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Paper Description

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The exponential growth in the availability of data requires that students are able to make informed decisions using data, and effectively communicate their data analyses.This course covers the analytical and statistical techniques that business and management students are most likely to use in their future courses and professional careers. Students will learn different types of data analytics methods and their applications to problems in accounting, economics, finance, marketing, and business in general.

This course uses a combination of lectures, case discussions, lab sessions and student presentations. Students will have hands-on work with data and Microsoft Excel. Weekly computer-based workshops aim to enhance understanding of how the techniques introduced in lectures apply in a business context. Topics to be covered include presenting data using visual and descriptive statistics, measuring and understanding the relationship between variables, predictive analytics and prescriptive analytics tools. Empirical examples from economics, finance, accounting, and marketing will illustrate the material covered. Emphasis will be placed on understanding concepts and analysis of data. The paper will also provide opportunities for students to enhance their teamwork and communication skills with an empirical group research project.

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Paper Structure

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This paper is delivered in a face-to-face format.

Two 3-hour in-class lectures and one 3-hour computer lab per week.

Lectures
The lectures will be focused on teaching quantitative methods to students who want to apply data and regression analysis techniques in the context of real-world empirical problems. They will also feature some discussion based on applied econometric theory, these will draw on practical examples that demonstrate the interpretation of results provided by various techniques.

Computer Labs
The main purpose of the computer labs will be to provide students with practical experience of using the econometric techniques covered in the lectures. In labs, you will be given a set of questions and exercises to complete using Microsoft Excel. The computer labs will also be a forum for students to discuss the lecture material and attempt various problem-solving exercises that might be set over the weeks.

In order to promote class participation and to provide immediate in-class feedback about specific concepts, we will use the Xorro-Q student response system. To participate, students will need an internet capable device (e.g. laptop, smartphone, tablet). The lecture theatres are Wi-Fi enabled and there are no data charges for accessing the Xorro-Q website on campus. Instructions on how to use Xorro-Q will be provided in class.

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Learning Outcomes

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Students who successfully complete the paper should be able to:

  • [1] Interpret business and economic data
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  • [2] Explain how data analytics theory applies to business decision making
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  • [3] Identify and apply the appropriate data analytics methods to real world business issues and interpret the results, including analysis of random experiments and methods of comparing groups
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  • [4] Make inference on population means, difference between means for business decision making
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  • [5] Use regression analysis and critically appraise the merits and shortcomings of using regression methods to analyse empirical data
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  • [6] Evaluate evidence to inform decision making
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  • [7] Demonstrate proficiency in using Microsoft Excel as a statistical and analytical tool
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Assessment

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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
9 Dec 2021
10:00 AM
20
  • Online: Submit through Moodle
2. Group Empirical Project
21 Dec 2021
5:00 PM
30
  • Online: Submit through Moodle
3. Class participation & In-Class Quizzes
10
  • In Class: In Lab
  • In Class: In Lecture
4. Final Test
16 Dec 2021
10:00 AM
40
  • In Class: In Test
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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Camm, J., Cochran, J., Fry, M., Ohlmann, J., Anderson, D., Sweeney, D., and T. Williams (2020) Business Analytics, 4th edition, Cengage Learning (On Course Reserve).

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Recommended Readings

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Duignan, J. (2014) Quantitative Methods for Business Research Using Microsot Excel, Cengage Learning (On Course Reserve)

Koop, G (2013) Analysis of Economic Data, Wiley (on Course Reserve)

Hyndman, R. and Athanasopoulos, G. (2018) Forecasting: Principles and Practice, 2nd ed., OTexts: Melbourne, Australia (freely available online at https://otexts.com/fpp2)

If you have a spare hour, we highly recommend watching The Joy of Stats, featuring the late Hans Rosling. His enthusiasm for statistics is infectious and his graphic data visualizations are terrific. You can stream the video here: https://www.gapminder.org/videos/the-joy-of-stats/

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Other Resources

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In addition to the required textbook, students are encouraged to read widely including the business section of newspaper, the Economist magazine, and other similar sources. Additional paper resources will be made available on Moodle.

The following websites are examples of Data Analytics being used in practice:

http://fivethirtyeight.com/

https://www.gapminder.org/

https://ourworldindata.org/

Waikato University also has several databases available for students with interests in Finance, which include:

  • NZX Company Research(formerly NZX Deep Archive), which provides historical information on New Zealand Companies
  • Datastream which provides key data sets from both developed and emerging markets. Current and historical data is available variables such as - equities, market indices, company accounts, economic indicators, bonds, foreign exchange, interest rates, commodities and derivatives.
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Online Support

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All course materials, plus other information of importance to students, are available via Moodle
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Workload

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According to the university points system, students are expected to spend 150 hours in total on a 15-point paper. 

Since you are taking a paper in Economics, we know that you will make rational and optimal (to you) decisions about the work you will put into the paper. Thus, the workload the paper places on you will depend on your preferences, what you want to achieve and the constraints you face. 

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Linkages to Other Papers

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Prerequisite(s)

Prerequisite papers: Acceptance into the MPAcct or MAppFin programme.

Corequisite(s)

Equivalent(s)

Restriction(s)

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