Pre-master Econometrics and Management Science

The pre-master Econometrics and Management Science prepares you for one of the specialisations within our MSc in Econometrics and Management Science.

The pre-master programme is taught in two languages. Please note that students who obtained their Bachelor's degree in the Netherlands can only participate in the English taught pre-master programme when the bachelor degree programme was taught entirely in English.

This pre-master is designed for students with a background in econometrics and operations research, economics and mathematics, quantitative economics, (applied) mathematics, statistics or industrial engineering that do not qualify for direct admission to any of the specialisations within the MSc in Econometrics and Management Science. Applicants with a Bachelor's degree in Economics, Business Economics or Business Administration need to have a quantitative specialisation (such as a minor or major in econometrics, operations research or decision analysis) in order to apply.

Pre-Master Econometrics and Management Science (NL)

De Take-Off is het introductieprogramma voor alle nieuwe studenten aan Erasmus School of Economics. Tijdens dit introductie evenement maak je kennis met je studie, je medestudenten en de faculteit. Het programma bestaat onder meer uit een studie introductie en een uitleg over de systemen.

Guidance sessions are meetings of a group of 15-30 students and their mentor. Mentors are senior students. There will be two guidance sessions in which a variety of subjects will be covered including practical information about the university and the study programme.

During the guidance sessions students play educational games to get to know their mentor, fellow students, and the university. Furthermore, they get information about how to complete several onboarding modules on Canvas. In these modules, they learn about important practical matters of studying at the ESE. Students are also offered the opportunity to have one or more individual meetings with their mentor to ask questions and/or discuss expectations.

Introduction to Programming (minor and pre-master)

The course starts by introducing the fundamental data types and operations. After that, control statements, i.e., decision and repetition, are presented. Then, methods, as computations consisting of multiple steps, are given. Lists and arrays, representing data structures storing multiple values, are depicted next. Last, the main concepts of object-oriented programming paradigm, i.e., class and instance, are described.

Meanwhile, students are expected to roll up their sleeves and acquire hands-on experience with the Java programming language. For this purpose, programming assignments will have to be completed by the students using a popular Integrated Development Environment (IDE). Students will develop an algorithmic way of thinking by implementing solutions for elementary computational problems in Econometrics.

Programmeren

  • Week 1: Opfrissen Object-geörienteerd programmeren met Java
  • Week 2: Werken met interfaces
  • Week 3: Werken met inheritance
  • Week 4: Werken met datastructuren uit de Collections API
  • Week 5-6: Libraries, moderne Java features
  • Week 7: Samenvatting, andere programmeertalen (geen tentamenstof)

Discrete and continuous random variables, moment generating functions, discrete and continuous joint distributions, independence , conditional distributions, conditional expectations, variance, covariance, functions of random variables, transformation methods, Central Limit Theorem, stochastic convergence.

A typical phenomenon in empirical research is the wish to make statements about an unobservable population on the basis of an observed sample. The course Statistics aims to facilitate the construction of statistical statements about the population on the basis of a random sample. The statements may concern statistical estimates or statistical hypothesis tests.

Moreover, the statistical statements are typically accompanied by information concerning the level of (un)certainty. In this course, the construction of "good" estimators and "good" tests in parametrical statistical models is considered. In particular, we will focus on the construction of method of moments estimators, maximum likelihood estimators, (uniformly) most powerful tests and likelihood ratio tests.

Van week tot week:

  1. Analysis of Unconstrained Problems
  2. Unconstrained Optimisation Methods
  3. Derivative-Free Optimisation
  4. Optimization with Sampling
  5. Analysis of Constrained Problems
  6. Constrained Optimisation Methods
  7. Distributed Optimisation

  1. Discrete-time Markov chains
  2. Exponential distribution and the Poisson process
  3. Continuous-time Markov chains
  4. Basic queuing theory (M/M/c and variants)
  5. Gaussian processes (Brownian motion and others)

(with applications in econometrics and operations research)

Het vakgebied econometrie wordt gekenmerkt door een combinatie van economische vraagstellingen, gebruik van empirische gegevens, toepassing van statistische en wiskundige methoden, en gebruik van software om modellen te schatten en te evalueren. Al deze onderwerpen komen uitgebreid aan de orde in de hoorcolleges en worden geoefend bij de sommencolleges en in de practica.

  • Hoorcollege en sommencollege: Inleiding econometrie, vraagstellingen, modellen, methoden. Het lineaire regressiemodel (enkelvoudig en meervoudig), methode van kleinste kwadraten, toetsen. Niet-lineaire modellen, maximale aannemelijkheid, enige asymptotische theorie van schatten en toetsen. De modellen en methoden worden gemotiveerd vanuit economische toepassingen en toegepast in praktische voorbeelden. De benodigde econometrische methoden maken intensief gebruik van eerdere vakken uit het programma, met name statistiek, lineaire algebra en analyse.
  • Practicum: Opgaven over theorie en toepassingen. Voorts wordt enige malen een computer-practicum gehouden, waar econometrische technieken worden toegepast met het pakket EViews.

Dit vak geeft een inleiding in de theorie en technieken van de lineaire programmering. Behandeld worden:

  • Modelleren van lineaire programmeringsproblemen
  • Oplossen van lineaire programmeringsproblemen
  • Primale en duale (gereviseerde) simplex methode
  • Dualiteitstheorie
  • Gevoeligheidsanalyse
  • Netwerk simplex methode

By means of practical case studies, attention will be given to the four different major tracks within the bachelor econometrics programme:

  • Students participate only in the case that fits with the profile of their pre-master programme
  • Different computer packages will be used
  • Presenting of research findings

Tijdreeksanalyse is het vakgebied dat zich bezighoudt met het modelleren van sequentiele waarnemingen van economische variabelen, zoals maandelijkse werkloosheidscijfers.
Een dergelijk model kan worden gebruikt voor het doen van voorspellingen en voor het inschatten van mogelijke gevolgen van beleid.
Centraal staat hierbij de dynamische samenhang tussen economische ontwikkelingen (bijvoorbeeld. trends, seizoeninvloeden, conjunctuurcycli).

De voornaamste onderwerpen die in dit vak aan bod komen zijn:

  • Theorie van stationaire processen
  • Lineaire tijdreeksmodellen (ARIMA)
  • De keus van de dynamische structuur van een tijdreeksmodel
  • Schatten van modelparameters
  • Evaluatie van tijdreeksmodellen
  • Omgaan met bijzondere waarnemingen (uitschieters)
  • Voorspelmethoden
  • Niet-lineaire tijdreeksmodellen

Studenten kiezen een specialisatie uit onderstaande lijst van specialisaties:

  • Inleiding multivariate statistiek
  • Micro-economie (econometrie)
  • Simulatie
  • Econometrie 2

  • Inleiding multivariate statistiek
  • Micro-economie (econometrie) of Finance (econometrie)
  • Simulatie
  • Econometrie 2

  • Inleiding multivariate statistiek
  • Micro-economie (econometrie)
  • Marketing (econometrie)
  • Econometrie 2

  • Combinatorisch optimaliseren
  • Micro-economie (econometrie) of Finance (econometrie)
  • Simulatie

Disclaimer
The overview above provides an impression of the curriculum for this programme. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR.

Pre-Master Econometrics and Management Science (ENG)

The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.

Guidance sessions are meetings of a group of 15-30 students and their mentor. Mentors are senior students. There will be two guidance sessions in which a variety of subjects will be covered including practical information about the university and the study programme.

During the guidance sessions students play educational games to get to know their mentor, fellow students, and the university. Furthermore, they get information about how to complete several onboarding modules on Canvas. In these modules, they learn about important practical matters of studying at the ESE. Students are also offered the opportunity to have one or more individual meetings with their mentor to ask questions and/or discuss expectations.

The course starts by introducing the fundamental data types and operations. After that, control statements, i.e., decision and repetition, are presented. Then, methods, as computations consisting of multiple steps, are given. Lists and arrays, representing data structures storing multiple values, are depicted next. Last, the main concepts of object-oriented programming paradigm, i.e., class and instance, are described.

Meanwhile, students are expected to roll up their sleeves and acquire hands-on experience with the Java programming language. For this purpose, programming assignments will have to be completed by the students using a popular Integrated Development Environment (IDE). Students will develop an algorithmic way of thinking by implementing solutions for elementary computational problems in Econometrics.

  • Week 1: Refresh Object-oriented programming Java
  • Week 2: Working with interfaces
  • Week 3: Working with inheritance
  • Week 4: Working with datastructes from the Collections API
  • Week 5-6: Libraries, modern Java features
  • Week 7: Summary, other programming languages (no exam material)

Discrete and continuous random variables, moment generating functions, discrete and continuous joint distributions, independence , conditional distributions, conditional expectations, variance, covariance, functions of random variables, transformation methods, Central Limit Theorem, stochastic convergence.

A typical phenomenon in empirical research is the wish to make statements about an unobservable population on the basis of an observed sample. The course Statistics aims to facilitate the construction of statistical statements about the population on the basis of a random sample. The statements may concern statistical estimates or statistical hypothesis tests.

Moreover, the statistical statements are typically accompanied by information concerning the level of (un)certainty. In this course, the construction of "good" estimators and 'good' tests in parametrical statistical models is considered. In particular, we will focus on the construction of method of moments estimators, maximum likelihood estimators, (uniformly) most powerful tests and likelihood ratio tests.

From week to week:

1. Analysis of Unconstrained Problems
2. Unconstrained Optimisation Methods
3. Derivative-Free Optimisation
4. Optimization with Sampling
5. Analysis of Constrained Problems
6. Constrained Optimisation Methods
7. Distributed Optimisation

  1. Discrete-time Markov chains
  2. Exponential distribution and the Poisson process
  3. Continuous-time Markov chains
  4. Basic queueing theory (M/M/c and variants)
  5. Gaussian processes (Brownian motion and others)

(with applications in econometrics and operations research)

Econometrics is characterized by the combination of economic research questions, use of empirical data, application of statistical and mathematical methods, and the use of software to estimate and evaluate models. All these topics are extensively discussed in the lectures and practised in the tutorials.

  • Lectures and exercise lectures: Introduction to econometrics, research questions, methods. The linear regression model (simple and multiple), method of least squares, testing. Non-linear models, maximum likelihood, some asymptotic theory of estimation and testing. The models and methods are motivated by economic applications and applied in practical examples. The required econometric methods make intensive use of earlier courses in the programme, in particular statistics, matrix algebra and analysis.
  • Tutorials: Exercises on theory and applications. Further, some tutorials consist of computer sessions to apply econometric techniques by means of the software package EViews.

This course is an introduction in the theory and methods to solve linear programming problems. The topics of the course are:

  • Modeling and solving linear programming problems
  • Primal and dual (revised) simplex method
  • Duality theory
  • Sensitivity analysis
  • Network simplex method

By means of practical case studies, attention will be given to the four different major tracks within the bachelor econometrics programme:

  • Students participate only in the case that fits with the profile of their pre-master program
  • Different computer packages will be used
  • Presenting of research findings

Time series analysis concerns modelling sequential observations on economic variables, such as monthly unemployment figures. A suitable time series model can be used for making forecasts and for policy analysis. A key issue in developing a suitable model for time series concerns the dynamic features of economic variables, such as trends, seasonal fluctuations, and business cycles.

The main topics covered in this course are:

  • Theory of stationary dynamic processes
  • Linear time series models (ARMA)
  • Model selection
  • Parameter estimation
  • Evaluating time series models
  • Handling special observations (outliers)
  • Forecasting methods and evaluation
  • Nonlinear time series models

Students choose one Specialisation from the specialisations listed below:

  • Introduction to Multivariate Statistics
  • Microeconomics (econometrics)
  • Simulation
  • Econometrics 2

  • Introduction to Multivariate Statistics
  • Microeconomics (econometrics) or Finance (econometrics)
  • Simulation
  • Econometrics 2

  • Introduction to Multivariate Statistics
  • Microeconomics (econometrics)
  • Marketing (econometrics)
  • Econometrics 2

  • Combinatorial Optimisation
  • Microeconomics (econometrics) or Finance (econometrics)
  • Simulation

Disclaimer
The overview above provides an impression of the curriculum for this programme. It is not an up-to-date study schedule for current students. They can find their full study schedules on MyEUR.

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