Applied Econometrics II : Time Series

Duration

30h Th, 15h Pr

Number of credits

Lecturer

Malka Guillot

Substitute(s)

Alain Hecq

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

This is a graduate course in applied time series econometrics. This course takes an inductive perspective to develop asignificant understanding of the role of time series econometrics in empirical economics and a strong ability to executeapplied time series econometrics in the development of economic models. Identification, estimation, evaluation, hypothesis testing, and forecasting will be emphasized.

The main problems which can be encountered in econometric modeling with macroeconomic time series will be first introduced, and practical examples will be given. Then, all the basic notions concerning time series will be addressed in a univariate framework. Formal examples as well as practical illustration on real financial and macroeconomic data will be given.

Finally, the course ends by an introduction to the multivariate framework. If time allows further useful methods but more recent methods for time series analysis will be covered, such as the synthetic control and maching learning methods.

Learning outcomes of the learning unit

At the end of the course the student is expected to be able to

Knowledge and understanding:

* Explain and describe the main statistical methods for time series analysis.

* Implement both univariate and multivariate time series models

* Select most appropriate model

* Test for non stationarity

* Modeling non stationary time series with trends or volatility

Applying knowledge and understanding:

* Use of a programming and statistical software: python and main python functions for time series analysis

* Apply and properly interpret the models and methods presented in the course in applications.

* Evaluate and justify their analysis on real data.

* Prepare appropriate reports of their statistical analysis in real data applications.

Prerequisite knowledge and skills

Students should be comfortable with undergraduate microeconomics, statistics and econometrics classes.

Planned learning activities and teaching methods

Meetings

There are meetings on Fridays afternoon with both lectures and practical/empirical sessions. Each week you will have empirical investigations to prepare (team homework) and to present at the beginning of the meeting the week after.

Lecture slides of the corresponding week will be uploaded before the meetings (I hope the week before). In each week, we focus on topics according to the following draft schedule.

• Week 1 (19/09):    Introduction to time series features, importance of non stationarity, brush up econometrics, intro to EViews.
• Week 2 (26/09):    Mispecification tests: autocorrelation, heteroskedasticity, non normality, non linearity. Discussion of the case from Week 1.
• Week 3 (3/10):      Unit root tests (deterministic versus stochastic trends). Discussion of the case from Week 2.
• Week 4 (10/10):    Deterministic components: seasonality, outliers, breaks. Discussion of the case from Week 3.
• Week 5 (17/10):    ARIMA modelling. Discussion of the case from Week 4 material.
• Week 6 (24/10):    Forecast comparison and combination. Discussion of the case from Week 5 material.
• Break: no course on 31/10 and 7/10 (preparation of mid block presentation)
• Week 7 (14/11):    Presentation of the first part of the research paper: univariate modelling.
• Week 8 (21/11):    Spurious regression and cointegration.
• Week 9 (28/11):    VARs and impulse responses. Discussion of the case from Week 8.
• Week 10: (05/12)  Futher topics: time varying volatility, mixed frequency models. Discussion of the case from week 9.
• December 18: deadline to submit the final paper.
• December 19: oral exam, namely a discussion on the final paper (with your teammate).

Mode of delivery (face to face, distance learning, hybrid learning)

Course materials and recommended or required readings

Platform(s) used for course materials:
- LOL@


Further information:

I do not really have a book that I follow; I mainly use my slides (and my oral non-recorded lectures).

I use some chapters in Diebold (2018), Forecasting. The book is available online (free download) at Diebold's webpage at http://www.ssc.upenn.edu/~fdiebold/Textbooks.html. The book is quite easy and provides different examples than mines.

You can catch up with econometrics for instance with Stock and Watson (2020), Introduction to Econometrics. We will also cover their Chapters 15, 16, 17.

Written work / report

Continuous assessment

Out-of-session test(s)


Further information:

Examination

The final mark will be the sum of your participation during the block (20%), your presentation at the mid block paper (20%) and the final empirical paper on which we will have an oral team discussion at the end (60%). No written exam.

Participation 20%: Active participation includes the study of the material, namely the preparation of the chapters, i.e. read assigned material (book chapters, slides) before/after (it depends on you actually) sessions start. Each week you will have an empirical investigation to prepare. Those assignments must be done in teams of two students. Starting week 2 to week 10 (not on week 7 and 8), each team should upload the outcome of its investigations. You get 1 point if you upload your document with the outcomes of your team investigations (pdf, doc). I take into account whether you did it seriously, not that it is correct because this is what we check during our meetings.

There are 7 of such discussions, hence you will receive the mark accordingly to the percentage of your participation.

Presentation 20%: In the middle of the block (week 7) you will have enough material to carry out on your own data the univariate investigation. On the 14th of November, each team will come to present those intermediary results. You will be judged on your ability to defend and explain your results as well as on the way you motivate and sell your analysis. Mistakes won't count much (big ones will) because you can fine tune those results in the final paper.

Paper 60%: There will be a final research paper (remaining 60%), to some extend the collection of your weekly investigations on the data of your choice (that will be explained in due course). This is to be done in groups of 2 students (same teammate or choose a different one in case of free riding) as well. The paper will be about: comparison of models, on data you chose, show that you picked up good models (no misspecifications), forecast comparisons and combinations,... The deadline to hand in the final paper is December 18. On the 19, we will have a team discussion on your results. This is ann oral exam but based on the results that you have obtained in your research paper (that you take with you). There are no fixed bounds for the number of pages. Can be 20 pages, can be more with the appendix. You are free, you should convince me about the outcomes of your investigation.

 

August session

Students have to contact the professor to arrange the hand-in and oral discussion on the final empirical paper (60%). The rest of the mark (20% participation and 20% presentation of the mid block paper) will be similar to the January session. 

Work placement(s)

Organisational remarks and main changes to the course

Contacts

• Lecturer: Prof. dr. Alain Hecq (a.hecq@maastrichtuniversity.nl)
• Assistant: Nicolas Marissiaux ( Nicolas.Marissiaux@uliege.be)

Association of one or more MOOCs