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Applied Quantitative Methods

Code: 2MEAE04     Acronym: MQA

Keywords
Classification Keyword
OFICIAL Mathematics

Instance: 2021/2022 - 1S Ícone do Moodle

Active? Yes
Course/CS Responsible: Master in Economics and Business Administration

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
EAE 42 Official Syllabus after 2021-2022 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Maria Margarida Malheiro Queiroz de Mello

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 2 6,00
Francisco Vitorino da Silva Martins 1,50
Maria Margarida Malheiro Queiroz de Mello 1,50
Natércia Silva Fortuna 0,00

Teaching language

Suitable for English-speaking students

Objectives

This course main goal is to show how quantitative methods can be applied to the analysis of management and economic problems and to the decision making process that leads to the best solution available. In a more practical and close to date framework, this course can also assist students to better appreciate published scientific research, and conveys the basic training for helping them to prepare the empirical part of their dissertations.

Learning outcomes and competences

By the end of this course, students are expected to be able to

i- Produce, estimate and interpret statistically and theoretically valid econometric models applied to management and economic contexts;

ii- Comprehend, and critically assess research articles in scientific journals;

iii- Appropriate use of the econometric software made available during the course.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Students attending this course are expected to have knowledge of Mathematics, Statistics and Economic Theory at the level of under-graduations in Economics and/or Business Management.

Program

Course Outline:

Module 1

1. Classic Linear Regression Model (CLRM)

Simple regression model analysis

OLS estimators’ properties under the classic hypothesis

Multiple regression model analysis

Alternative linear models and variable transformation

Hypothesis testing for single coefficients and groups of coefficients

2. Binary (dummy) variables

Intercept, slope and interactive dummy variables

The base-class and the dummy variable ‘trap’ 

Economic interpretation of the coefficients of dummy variables

Structural break tests with dummy variables.

3. Relaxing the classical hypothesis of the CLRM

Heteroscedasticity: detection, problems and solutions

Serial correlation: detection, problems and solutions

Multicolinearity: detection, problems and solutions

Specification errors: “detection”, problems and solutions

Hendry’s (1995) general-to-specific approach

 

Module 2

1. Stationarity, cointegration and spurious regression

Stationarity analysis: unit root tests

Spurious regression, cointegration and the Error Correction Model (ECM)

Granger causality and cointegration

Vector error correction models (VECM) and the Johansen method

2. Binary choice models

Linear probability models

Logit e probit models

Evaluation and statistical analysis of binary choice models

 3. Linear models with panel data

Random effects model

Fixed effects model

First differences model

Fixed effects versus first differences

Fixed effects versus Random effects

Mandatory literature

Oliveira, Manuel José Mendes de; Econometria. ISBN: 978-972-592-326-9
Gujarati, Damodar N; Basic econometrics. ISBN: 9780071276252

Complementary Bibliography

Dougherty, Christopher ; Introduction to Econometrics, Oxford University Press, 2007
Wooldridge Jeffrey M.; Econometric analysis of cross section and panel data. ISBN: 0-262-23219-7

Teaching methods and learning activities

Theoretical presentation of the matter/issue under concern, followed by practical examples for the students to solve, preferably with actual data on the subjects addressed.

Software

EViews
Software R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Teste 100,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 4,00
Total: 4,00

Eligibility for exams

Normal Season: Distributed evaluation without final exam

There are two ways of passing this course:

1st - To write two tests: one corresponding to Module 1 and the other corresponding to Module 2.
The student choosing this form of evaluation will pass the course if:
a) The mark in each of the tests is not below 6/20.
b) If the weighted average mark of both tests is equal or higher than 9.5/20 marks (the tests have equal weight)

2nd - To write the final exam. The student choosing this way will pass if the mark of this exam is equal or higher than 9.5/20.

NOTES:

  1. The first test evaluates matters addressed in the first module and the second test evaluates the matters addressesd in the second module. Both tests take place in the same date as the 'normal season' exam with an intervale of 15 minutes between the two.
  2. Not writtging one of the tests is equivalent to obtaining a zero mark for that test.
  3. Students with a mark below 6/20 in any of the tests will not pass the course, independently of the weighted average obtained.

Resit Season: Final exam. To pass this exam the student must obtain a mark equal or higher than 9.5/20.

Calculation formula of final grade

COMPUTATION OF THE  FINAL GRADE FOR THE DISTRIBUTED EVALUATION:

Mark of module 1 test (T1)
Mark of module 2 test  (T2)

Final Grade = 0.5*T1 + 0.5*T2

Examinations or Special Assignments

NA

Internship work/project

NA

Special assessment (TE, DA, ...)

In accorance with the evaluation regulations of FEP.UP.

Classification improvement

In accorance with the evaluation regulations of FEP.UP.
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