Semester Offering: InterSem
 

With increasing needs for research evidence generated through sound quantitative analysis, it is becoming a requirement for students to have basic understanding and practical experience with selected techniques in quantitative analysis. The objective of this course is to equip students with fundamental skills in handling useful analytical techniques, so that they can examine their research questions and hypotheses through these tools while conducting their research. In particular, the course introduces a set of basic regression models, which enable the students to implement analysis of causality from independent variables to a dependent variables. The course emphasizes application rather than theories, and analysis rather than data collection.

 

Upon successful completion of this course, students will be able to:

  • Select and apply basic but useful and relevant analytical methods for research, using R software; and
  • Interpret the output of the analysis in a quantitative manner, while noting the limitations

 

None

 

  1. Introduction to R
    1. Basic operations for data
    2. Procedures for generating descriptive and inferential
  2. Quantitative analysis for cross-sectional
    1. Review of simple (two-variable) linear regression with the least squares
    2. Multiple linear regression with OLS: the concept of ceteris
    3. Some techniques within OLS: dummy explanatory variables, interaction terms, quadratic terms, and
    4. Required conditions for OLS and basic ideas of estimation
    5. Selected models for limited dependent variables: censored and truncation variable (tobit), binary variable (probit, logit, LPM)

 

Classroom (or online) sessions involve lectures and hands-on exercise with students’ own computers. Lectures and practices go hand in hand.

 

 

  1. Stock, JH and Watson, MW (2017) Introduction to Econometrics, 3rd edition, Addison- Wesley International.
  2. Gujarati, D. (1999) Essentials of Econometrics, 2nd edition, McGraw-Hill.

 

  1. Venables, WN, Smith DM, and the R Core Team (2017) Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics. Manual for R version 3.4.3. R, Foundation for Statistical Computing. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
  2. Wooldridge, JM (2003) Introductory Econometrics: A Modern 2nd edition, South- Western College Publishing.
  3. Kennedy, P (1998) A Guide to 4th edition, Blackwell.

 

  1. International Journal of Social Research Methodology (Taylor & Francis)
  2. The R Journal (The R Foundation)

 

Lecture: 10 hours

Practices with own computers in classroom (or virtual classroom): 15 hours

Self-study: 30 hours

 

 

Lectures and practices in the classroom (or virtual classroom). Selected quantitative techniques will be illustrated by using Open-Access R software.

 

 

The term paper carries 90% of the weight, while participation carries 10% of the weight. There will be no exam. Class attendance will not be taken.

Grade “A” would be awarded if a student demonstrates excellent understanding on topics covered in the course.

Grade “B” would be awarded if a student shows an overall understanding of topics covered in the course.

Grade “C” would be given if a student meets below average expectations on both understanding and application.

Grade “D” would be given if a student does not meet basic expectations in analyzing or understanding issues covered in the course.