During the mini-course we are going to use, aside from this website, the following:

Resources:

• Aula Virtual site for Macroeconometrics: from here we will use Black Board Collaborate to make on-line meetings.

• Jitsi link to make e-meetings. If for some reason the Black Board application fails, then we will make the e-meetings using Jitsi. Be aware that to join the Jitsi-room you will need a password. If finally we have to use Jitsi I will send you the password through your UV e-mail.

• Etherpad: during the course we are going to make some work with R, we will use an etherpad to collaborate with the code.

• RStudio Project in the cloud: only to be used for the students that could have some problems with his own computer because they could not install the packages properly. That’s it, 99,999% that we are not going to use this Rproject in the cloud!!

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Materials:

• Slides: the slides that I used last year to teach the course. Last, and previous, years the teaching was not on-line.

• Lab. A document that replicates the Gali(1999) paper, providing an example of use structural VAR methodology using R and the vars package.

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More resources:

The assignment of the course should be delivered as an Rproject and written in Rmarkdown, then you should know what they are. Don’t worry I will explain this the first day. It’s funny and really useful. You could read the following two tutorials. They are written in Spanish because they come from another course I have. Sorry but I have no time to translate them, but internet is plenty of resources in English.

• Tutorial about Rprojects: section 3 of this tutorial explain how to make an Rproject and why it is useful. It has also references in English, for instance this one.

• Rmarkdown Tutorial: an introduction to Rmarkdown. Also plenty of references in English, for instance this one.

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Bibliography

The slides and the VAR mini-course are based on the following documents:
(and probably some others that I have not remembered at the time of the final making. Thanks to all of them)

• Amisano, G., and Giannini, C. (1997). Topics in Structural VAR Econometrics. Springer.
• Bjørnland, H. C., and Thorsrud, L. (2015). Applied Time Series for Macroeconomics, Gyldendal Akademisk.
• Gali, J. (1999), Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?, American Economic Review, 89, 249-271.
• Gottschalk, J. (2001), An Introduction into the SVAR Methodology: Identification, Interpretation and Limitations of SVAR models, Kiel Working Paper nº 1072
• Kilian, L. (2011), Structural Vector Autoregression, CEPR Discussion Paper Series n^o 8515.
• Sims, C.A. (1980), Macroeconomics and Reality, Econometrica, 48, 1-48
• Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis. Springer: New York.
• Lütkepohl, H. (2011), Vector Autoregressive Models, EUI Working Paper ECO2011/30
• Pfaff, B. (2008a), VAR, SVAR and SVEC Models: Implementation Within R Package vars, Journal of Statistical Software, 27(4)
• Pfaff, B. (2008b), Analysis of Integrated and Cointegrated Time Series with R, Springer
• Pfaff, B. (2014), Package ‘vars’, CRAN R package.
• Stock, J. H. (2008), Recent Developments in Structural VAR Modelling, NBER Summer Institute

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