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Quantitative Methods for Decision Making - Modulo Data Analysis

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Quantitative Methods for Decision Making - Modulo Data Analysis

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Academic year 2020/2021

Course ID
MAN0523B
Teacher
Arthur Van Soest (Lecturer)
Degree course
BUSINESS ADMINISTRATION - Curriculum: Business Management and International Marketing
BUSINESS ADMINISTRATION - Curriculum: Finance and Accounting
Year
1st year
Teaching period
To be defined
Type
Distinctive
Credits/Recognition
5
Course disciplinary sector (SSD)
SECS-S/01 - statistica
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Written
Type of learning unit
modulo
Modular course
Quantitative Methods for Decision Making - Integraded course (MAN0523)
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Sommario del corso

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Course objectives

Basic economics knowledge
• Basic mathematical tools & some calculus (Wooldridge, Appendix A)
• Linear algebra (Wooldridge, Appendix D & Appendix E)
• Fundamentals of probability theory (Wooldridge, Appendix B)
• Fundamentals of mathematical statistics (Wooldridge, Appendix C)
• Computer skills
• The statistics/econometrics software package Stata

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Results of learning outcomes

Becoming confident with econometrics and familiar with the statistics/econometrics software package Stata

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Program

A provisional overview of how the lectures (8 lectures of 2 hours each) match to the textbook is given below.
Lecture 1
Chapter 1: The nature of econometrics and economic data
- What is econometrics?
- Examples of empirical questions
- Steps in empirical economic analysis
- The structure of economic data
Chapter 2: The simple linear regression model
- Data and econometric model
- OLS estimator
- Model assumptions
- Properties of OLS
- Expected value and variance OLS estimator
We will skip Section 2.5 and discuss this as a special case of Chapter 3
Introduction Chapter 3: Multiple linear regression model in vector and matrix notation
Computer exercises
Data description; estimation of the standard linear model, prediction. Application: Flat prices in Moscow
Lecture 2
Chapter 3: Multiple regression analysis: estimation
- Model assumptions
- Mechanics and interpretation of OLS
- Goodness-of-fit
- Prediction
- Expected value and variance of OLS estimator
- Gauss-Markov theorem
Section 3.2: skip “A “Partialling Out” Interpretation of Multiple Regression”;
Section 3.3: skip “Omitted variable bias: the simple case” and “Omitted variable bias: more general cases”
Section 3.4: skip “Variances in Misspecified Models”
Introduction Chapter 4: Statistical inference in the linear regression model
Computer exercises
Estimation of the standard multiple linear regression linear model, prediction. Application: Flat prices in Moscow
Lecture 3
Chapter 4: Multiple regression analysis: inference
- Sampling distribution of OLS estimator
- Testing hypothesis about single population parameter (t-test)
- Confidence intervals
- Testing multiple linear restrictions (F-test)
Computer exercises
Estimation and testing; t-test; F-test; model F-test. Application: Satisfaction with household finances;
Lecture 4
Chapter 6: Multiple regression analysis: further issues
- Effects of data scaling on OLS statistics
- More on functional form
- Prediction and residual analysis
Skip Section 6.3; Section 6.4: skip “Predicting y when log(y) is the dependent variable”
Chapter 7: Multiple regression analysis with qualitative information: binary (or dummy) variables
- Describing qualitative information
- Dummy independent variables (including multiple categories)
- Interactions involving dummy variables
- Logarithms, squares, and other functional form issues
- A binary dependent variable: linear probability model
Skip Section 7.6
Chapter 5: Multiple regression analysis: OLS asymptotics
- Law of large numbers and central limit theorem
- Consistency
- Asymptotic normality and large sample inference
- Asymptotic efficiency of OLS
Section 5.2: skip the second part (“Other large sample tests…”)
Introduction to non-standard linear regression models: Relaxing the assumptions
Computer exercises
The standard linear model: statistical inference, model selection, prediction intervals. Application: Wage differentials between ethnic groups in Malaysia.
Lecture 5
Chapter 8: Heteroskedasticity
- Generalized linear model with heteroskedasticity
- Consequences for OLS
- Testing for heteroskedasticity
- Heteroskedasticity-robust inference after OLS estimation
Section 8.2: Skip the last part (“Computing heteroskedasticity-robust LM Tests”)
Skip Section 8.4; skip Section 8.5
Excerpts from Chapter 10: Basic regression analysis with time series data, and Chapter 11: Further issues in using OLS with time series data.
- Nature of time series data
- Examples of time series regression models
- Finite sample properties of OLS under classical assumptions
- Trends and seasonality
- Stationary and weakly dependent time series
- Asymptotic properties of OLS
Only the following sections: 10.1, 10.2, 10.3, 10.5, 11.1, 11.2
Computer exercises
The generalized linear model with heteroscedasticity; linear probability models; time series models. Applications: School continuation decisions at age 16; Advertising and sales.
Lectures 6 and 7
Chapter 15: Instrumental variables estimation and two-stage least squares
- Motivation and examples
- Properties of OLS
- IV estimation of the multiple regression model
- Two-stage least squares
- Testing for endogeneity
- Testing for instrument validity
- Simultaneous equations model
Only the following sections: 15.1, 15.2, 15.3, 15.4, 15.5
Computer exercises
Estimation and testing in models with potentially endogenous regressors. Applications: Job satisfaction and work hours; immigrant wages and speaking fluency.

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Course delivery

It is sufficient to know everything discussed during the lectures, the tutorials and the computer classes. This corresponds with the chapters and sections in the book given below, except that we used matrix notation and the book does not.

 

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Learning assessment methods

USEFUL INFORMATION ON EXAMS POST-COVID EMERGENCY

WRITTEN PART

  1. You have to connect to Webex (through the invitation link that has been sent to you by Prof. Basiglio) 30 minutes before the official start of the exam so that we can start on time.
  2. Get equipped with a webcam; you will have to connect with the webcam and you will have to show us your “place of examination” so to control that the exam will be conducted in fair conditions for everyone; the use of the webcam is COMPULSORY: if some of you disconnect the webcam the exam will be null.
  3. Stay prepared with the smart card that can identify yourself.
  4. You will be allowed to have with yourself 5 sheets of A4 paper, pen(s) and a calculator (we will control for that before starting the exam).
  5. The text of the exam will be downloadable from Moodle in the new section “ONLINE EXAMS QMDM”.
  6. The structure of the exam is pretty much the same as that of past exams. You will have about 120 minutes for the examination during which you have to keep webcam and microphone ON.
  7. At the end of the exam, you have to scan or take pictures of your exam while connected on Webex (if someone interrupts the connection, the exam will be considered null).
  8. The scanned pictures have to be attached in a single file in PDF format named “SURNAME QMDM dd_mm_2020” and have to be uploaded on Moodle (where you found the text of the exam) by clicking on “Submit/Consegna compito”.
  9. To do this, you will have 10 minutes after the end of the exam. If by then, the exam is not uploaded on the platform, your exam will not be evaluated.

SHORT ORAL on the day/some days after the written part

You will have a Skype call with Prof. van Soest (or you will be contacted through Zoom by the professor) to take a short oral exam so to have a look at the exam; students might be asked for some clarifications about the answers they gave or some other questions about the program done so far.

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Support activities

See also the Online Appendix to the book available at

http://www.cengagebrain.com/cgi-wadsworth/course_products_wp.pl?fid=M20b&product_isbn_issn=9781408093757&token=D0471F3DC97D707874DA394B6BE5ADDB93E053F47E08D6C5F12E4C0F769D6778107EE7A3E8423077&template=EMEA

In particular, we will use matrix and vector notation, as in Appendix E of the book. Appendices A – D provide a good overview of the concepts and tools from mathematics and statistics that we will need in the course. These are essentially the prerequisites of the course.

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Suggested readings and bibliography

Textbook: Jeffrey M. Wooldridge (2014), Introduction to Econometrics. A Modern Approach, Cengage Learning

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Notes

The teaching activities modalities may vary according to the limitations imposed by the current health crisis. In any case, lectures will be online throughout the academic year.

 

Le modalità di svolgimento dell'attività didattica potranno subire variazioni in base alle limitazioni imposte dalla crisi sanitaria in corso. In ogni caso è assicurata la modalità a distanza per tutto l'anno accademico.

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    01/03/2020 at 00:00
    Enrollment closing date
    31/12/2022 at 23:55
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