Premaster EMAS

A new generation of actuaries

Our premaster programme (PrEMAS) is your ticket to the EMAS and excellent career prospects. To know which requirements you need to meet for admission to the master’s programme EMAS, please contact the programme coordinator for a personal advice based on your previous education. This study advice is free of charge and without any obligations.

 

You need to have a (quantitative) bachelor’s degree to be admitted in our premaster programme. Your personal programme will be composed of one or more modules of the following six subjects and in some cases students can enter the EMAS directly:

 

  1. Mathematical methods for actuaries
  2. Quantitative & Corporate Finance
  3. Probability theory and statistics for actuaries
  4. Econometrics and Academic Skills
  5. Actuarial Science
  6. Macro- & Microeconomics

 

Lectures in the study year 2021-2022 take place on Mondays (afternoon & evening), except for PrEMAS 4 (Tuesday evening) and start on Monday 30 August (PrEMAS 6) and Tuesday 31 August for PrEMAS 1. The remaining lectures of PrEMAS 1 are on Mondays.

 

More information

 

or contact our programme coordinator Leandra Pennartz (030-6866190). 

 

Covid-19 policy: more information (in Dutch)

 

PrEMAS 4 - Econometrics and Academic Skills

Level: The course is taught at the academic level of bachelor Econometrics

 

Module description

This course provides an introduction to commonly used econometric methods. The first part focuses on the theory and application of the linear regression model and covers the assumptions that are required for the identification and estimation of the model parameters. The students will derive the asymptotic properties of the ordinary least squares estimator and will learn how to interpret results, assess the validity of a chosen model, test hypotheses and make predictions.

In the second part of the course, the students will learn about methods that can be used when some of the assumptions underlying the linear regression model do not hold. Maximum likelihood estimation is applied to estimate models with binary outcomes (logit, probit) or with censored outcome data (Tobit). Methods that can be used to model stationary time series are also covered in this part.

 

Throughout the course, students will apply the methods to data from the financial, actuarial and economics domain using statistical programming language R. During these exercises we will discuss principles of modelling, sensitivity analysis, assumption checking, interpretation of the model output and reproducibility of results.

 

Entry requirements

It is strongly advised that the student has finished the course PrEMAS 1: Mathematical Methods (or equivalent) and that the student has followed or follows the course PrEMAS 3: Probability Theory and Statistics (or equivalent).

 

Learning objectives

Upon successful completion of the module, the student is able to:

  • Give examples of the types of questions that can be answered using econometric techniques and the types data that can be used to answer these questions;
  • Analyse linear relationships between variables using correlations and regression modelling;
  • State the assumptions of the linear regression model and explain when these assumptions may be violated;
  • Derive finite sample statistical properties of the ordinary least squares estimator;
  • Interpret the results of a linear regression model and explain how variable transformations can be used to alter the interpretation;
  • Estimate parameters for these models and perform diagnostic tests including checking assumptions and evaluating model fit;
  • Perform variable selection and engineering (e.g. dummy variables, interactions) and test for misspecification of the functional form;
  • Perform hypothesis testing on the values of the parameters;
  • Explain the impact of omitted variables on the unbiasedness of the least squares estimator;
  • Explain the impact of heteroskedasticity, multi-collinearity and autocorrelation on the parameter estimates and their standard errors and propose potential solutions;
  • Derive maximum likelihood estimators for the linear regression and for limited dependent variable models (e.g. probit for binary outcomes and Tobit for censored data);
  • Apply the likelihood ratio test to compare nested models;
  • Describe and apply the main concepts underlying stationary time series models;
  • Characterise the stationarity of an autoregressive moving-average (ARMA) process through the roots of lag polynomials;
  • Identify when an ARMA model for time-series data is appropriate, estimate the parameters and interpret the results;
  • Derive one-step-ahead and multiple-step-ahead forecasts and prediction intervals for an ARMA model;
  • Explain the difference between the short-run and long-run properties of a model, and how this may be relevant in deciding whether a model is suitable for any particular application;
  • Describe, in general terms, how to decide whether a model is suitable for any particular application;
  • Describe, in general terms, how to analyse the potential output from a model, and explain why this is relevant to the choice of model;
  • Carry out sensitivity and stress testing of assumptions and explain why this forms an important part of the modelling process;
  • Produce an audit trail enabling detailed checking and high-level scrutiny of model;
  • Explain the factors that must be considered when communicating the results following the application of a model and produce appropriate documentation;
  • Plan and execute a simple empirical research project and document the analysis and the research findings in a scientific paper.

 

Important information

  • Lectures: Tuesday evening, 18.00 - 21.00. First lecture on Tuesday 4 May 2021.
  • Assessment: Written exam and take-home assignment.The exam is on 18 October and the resit on 30 November 2021.
  • Costs for the module €3000.

    Literature
  • Introduction to Econometrics EMEA Edition, 1st Edition, Jeffrey M. Wooldridge, CENGAGE Learning, 2014. ISBN: 978-1-4080-9375-7 

 

Software

For the assignments we will use the statistical programming language R (https://www.r-project.org/) and RStudio (https://www.rstudio.com/). It is expected that you have working installations of R and RStudio on your laptop before the start of the first lecture. Please contact the lecturer beforehand if you encounter installation issues.

 

Lecturer

Jelmer Ypma

PrEMAS 4 - Econometrics and Academic Skills

Kosten module inclusief examen€ 3075
Kosten herexamen€ 200
Alle kosten zijn inclusief modulekosten, weblectures en toegang tot de digitale leeromgeving en het examen. De kosten zijn exclusief het aan te schaffen lesmateriaal, zie hiervoor het tabblad lesstof.

Voor een examen of herexamen dient u zich apart in te schrijven.

Deze prijzen gelden voor het studiejaar 2021-2022 en zijn onder voorbehoud van wijzigingen.

Op deze kosten zijn de Algemene Voorwaarden van het Actuarieel Instituut van toepassing.

Mandatory literature to be acquired by the student:
 

  • Introduction to Econometrics EMEA Edition, 1st Edition, Jeffrey M. Wooldridge, CENGAGE Learning, 2014. ISBN: 978-1-4080-9375-7 
Lesrooster
titellocatiedatum
PrEMAS 4 Econometrics and Academic Skills10-05-2022

Examenrooster
titellocatiedatum
Examen PrEMAS 4 Econometrics and Academic SkillsJohan de Witt huis (Utrecht)30-11-2021
HERExamen PrEMAS 4 Econometrics and Academic SkillsJohan de Witt huis (Utrecht)30-11-2021
Examen PrEMAS 4 Econometrics and Academic SkillsJohan de Witt huis (Utrecht)06-10-2022
HERExamen PrEMAS 4 Econometrics and Academic SkillsJohan de Witt huis (Utrecht)17-11-2022