Learning objectives

This course aims to cover the main ideas and theoretical results employed in applied business economics research that employs “Advanced quantitative research methods”. The first part of the course is based on Stata, and it focuses on the econometrics of micro data. This part follows closely the textbook by Cameron and Trivedi “Microeconometric Using Stata”, 2010 edition by Stata Press (here abbreviated as MUS). Students are strongly encouraged to browse the content of the Stata YouTube channel with screencast tutorials to be ready for classes.
The second part of the course is modular, and it deals with specific methods, such as process model and process analysis, structural equation modeling, and experimental design, with specific replications of case studies.

Program

Class 1 (MUS ch3) (4 L + 1 P)
Linear models and testing.
Class 2 (MUS ch5 part 1) (4 L + 1 P)
GLS, FGLS, WLS.
Class 3 (MUS ch5 part 2) (4 L + 1 P)
Systems of linear regressions, Survey Data.
Class 4 (MUS ch6) (4 L + 1 P)
Endogeneity & Linear instrumental variables.
Class 5 (MUS ch8) (4 L + 1 P)
Panel data (Part 1)
Class 6 (MUS ch9) (8 L + 1 P)
Panel data (Part 2)
Class 7 (MUS ch10-11) (4 L + 1 P)
NLReg, optimization and testing.
Class 8 (MUS ch14-15) (4 L + 1 P)
Binary and multinomial models.
Class 9 (6 h)
Process model & process analysis. Case study replication
Class 10 (6 h)
Structural equation modeling
(CB-SEM & PLS-SEM)
Class 11 (6 h)
Experimental design and analysis.

Didactic methods

This course is designed to cover in some depth and with practical applications the main topics of a standard advanced graduate course in micreconometrics for business studies and to lead students to the mastering of use of Stata and other software packages used in quantitative business research. Students need to study these topics before attending lectures dealing with each topic.
Classes will be both theoretical and practical. Practice will be given using specific software products, which are dominant in business economics research and microeconometric analysis. Specifically, Stata is a programmable software, which is fully supported by a wide community of researchers meeting regularly in regional conferences (e.g. Stata conferences) and by a periodical journal where new specialized packages are being discussed and made available to users with examples. Furthermore, the Stata software, like R-markdown, for R (see this if you are into R) has its own version of markdown called MarkStat, which enables the creation of replicable data analysis and dynamic documents. These make the documentation of quantitive work much easier, so that researchers can easily communicate to co-authors, collaborators and research students. Several screencasts are available from the official Stata site which can help you have a broad idea of the commands and purpose of the tools, although you will need to download the manuals from the Stata website to have a better understanding and a set of references to the seminal papers. Some universities also provide free Stata resources, such as UCLA. There is a basic introduction to new users on Medium here.
Finally, some good research students have provided dedicated YouTube channels (note: these are not peer-reviewed, so they may contain errors), such as Felix Larry Essilfie, who has some videos on data management and logistic regression, and Bob Wen, who has put together a few playlists with some videos on difference-in-difference, SEM and even solutions to the problem sets in MUS. A good introduction is also available here. James Gaskins’ channel provides a comprehensive series of videos on SEM software applications, specifically SPSS (Amos)

Learning assessment procedures

GRADE COMPONENTS:
1. Class Preparation, Participation, (40%)
2. Assignments & Final take home exam, (60%)

Assessment

GRADING SCALE:
A. Excellent
B. Very good
C. Good
D. Sufficient
E. Not sufficient.
IN-CLASS ACTIVITIES:
Presentations, open discussion, and some practice.

Scheduled Lessons