Applied Macro-econometrics

Course ID: 
Year of Study: 

Description/ Objectives 

  • understand some of the pitfalls, problems, and solutions that arise in applied macroeconomic work
  • critically evaluate applied macro-econometric research
  • be familiar with the main approaches to modelling macroeconomic data
  • understand the formal and practical aspects of important macro-econometric methods.
  • apply analytical methods and recognize their limitations, solve problems associated with identification using macroeconomic data
  • be able to report empirical research results obtained using the methods covered.
  • perform analyses with macroeconomic data using gretl and R and to interpret gretl output as well as output in R
  • be able to replicate and present macroeconomic research papers that will enhance their ability to write an advanced high-quality dissertation

Main software is "GRETL" - Instructions (Updated Oct 01, 2022)

Install the open source software gretl ( ) in your computer (not for ipads or mobile phones). If you work on windows, go to  and download the self-installer (64-bit)



self-installer (64-bit)

latest release (Aug 9, 2022)




Also (from the same site download and install for practical purposes 

X-12-ARIMA (older version of the above)



TRAMO/SEATS (seasonal adjustment, ARIMA models)



(Alternatively/ALSO) you can use R  - Instructions (Updated Oct 01, 2022)

Computing language R using R-Studio (RStudio is an integrated development environment (IDE) for R)

Download and install the last version of R for Windows ( R-4.2.1 for Windows (32/64 bit) ). Webpage and Download R 4.2.1 for Windows (75 megabytes, 32/64 bit) 

Then download and install RStudio (it's easier to use R with it... includes a code editor, visualization tools, debugging etc.). If you use Windows: (or from site Download the RStudio IDE - RStudio)

RStudio is an integrated development environment (IDE) for R.

Course Contents

(A) Introduction - software, databases, time series, business cycles
(B) Stationarity/Non-stationarity: Vector AutoRegressive models (VAR models), Vector Error Correction models (VEC models)
(C) Structural Vector AutoRegressive models (SVAR)
c1. Impulse response function
c2. Forecast error variance decomposition
c3. Historical decomposition
c4. Identification strategies
c41. Zero Short-Run restrictions
c42. Zero Long-Run restrictions
c43. Medium Run restrictions
c44. Sign Restrictions
c45. Testing Invertibility
c46. External Instruments
(D) Applied Panel methods
(E) High-dimensional datasets. Factor models.

(A) Detailed notes and notes on forecasting. Also
Owyang, Michael and Sekhposyan, Tatevik, (2012), Okun’s law over the business cycle: was the great recession all that different?, Federal Bank of St. Louis Review, issue Sep, p. 399-418.
Stock, J.H. Watson, M.W. (2001). Vector Autoregressions. Journal of Economic Perspectives
Stock, J., and Watson M (2012): Disentangling the Channels of the 2007-2009 Recession. Brookings Papers on Economic Activity
Lettau, M., & Ludvigson, S., (2004). Understanding Trend and Cycle in Asset Values Reevaluating the Wealth Effect on Consumption. American Economic Review
Lettau, M., & Ludvigson, S., (2004). Expected returns and expected dividend growth - Journal of Financial Economics
Lettau, M., & Ludvigson, S., (2001). Consumption Aggregate Wealth and Expected Stock Returns - Journal of Finance
The SVAR addon for gretl (detailed pdf file on SVAR applications)
Lawrence H. Summers (1991). The Scientific Illusion in Empirical Macroeconomics. The Scandinavian Journal of Economics 93(2), pp. 129-148. url:
Paul Romer (2016). The Trouble With Macroeconomics. and
V.A. Ramey (2016). Chapter 2 - Macroeconomic Shocks and Their Propagation. In: ed. by John B. Taylor and Harald Uhlig. Vol. 2. Handbook of Macroeconomics. Elsevier, pp. 71-162. (sections 1 and 2 only)
Emi Nakamura and Jon Steinsson (2018). Identification in Macroeconomics. Journal of Economic Perspectives 32(3), pp. 59-86.
James H. Stock and Mark W. Watson (2018). Identication and Estimation of Dynamic Causal Effects in Macroeconomics Using External Instruments. The Economic Journal 128(610), pp. 917-948.
Identification - Narrative approach
Christina D. Romer and David H. Romer (2010). The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks. American Economic Review 100(3), pp. 763-801.
Valerie A. Ramey (2011). Identifying Government Spending Shocks: It's all in the Timing. The Quarterly Journal of Economics 126(1), pp. 1{50. url:
Ilzetzki, Ethan & Mendoza, Enrique G. & Végh, Carlos A., 2013. "How big (small?) are fiscal multipliers?," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 239-254.
Michael W. McCracken and Serena Ng (2016). FRED-MD: A Monthly Database for Macroeconomic Research. pp. 574-589

Learning objectives

Search for, analysis and synthesis of data and information, with the use of the necessary technology
Adapting to new situations
Working independently
Team work
Project planning and management
Respect for difference and multiculturalism
Criticism and self-criticism
Production of free, creative and inductive thinking

Teaching Method

Lectures (4 hours per week x 13 weeks) 52
laboratory practice 20
study and analysis of bibliography 70
interactive teaching 13
project, essay writing 45

Evaluation Method

Class Participation: 10%
Problem sets. Various problem sets during the course (multiple choice questionnaires, short-answer questions, open-ended questions) and short presentations of projects (problem solving, written work, essay/report, public presentation): 20%
Mid-term exam: 25%, (multiple choice questionnaire, short-answer questions, 2 hours)
Final Exam: 45%, 2 hours

Total: 100%

Course Info

Teaching Hours: 
4 hours per week
Teaching Credits: 

Current Tutors


Venetis Ioannis

Associate Professor
Venetis Ioannis
Field of Expertise: 
Organic Unit / Lab: 
"Quantitative Economics and Information Systems" Research Laboratory
Office Hours: 
(Postgraduate Studetns) Wednesday 14:00 - 17:00


Reading Recommendations: 
Heij & de Boer & Franses & Kloek & van Dijk (2004). Econometric Methods with Applications in Business and Economics, OUP, ISBN: 9780199268016
Structural Vector Autoregressive Analysis' by Lutz Kilian and Helmut Lütkepohl, Cambridge University Press, 2017
Lütkepohl H., (2007). New Introduction to Multiple Time Series Analysis. Springer Berlin Heidelberg New York
Lütkepohl H. & Krätzig, Μ., (2004). Applied Time Series Econometrics. Edited by: Lütkepohl Helmut and Markus Krätzig. Cambridge University Press
Hamilton, J.D., (1994). Time series Analysis. Princeton, NJ: Princeton University Press.
Forecasting: Principles and Practice (3rd ed) - Hyndman & Athanasopoulos - -
Enders, W., (2014). Applied Econometric Time Series, 4th Edition, Wiley, ISBN: 978-1-118-80856-6
Bibliography Recommendations: 
Online books: John Cochrane (Chicago), Time Series for Macroeconomics and Finance
Online books: D.S.G. Pollock (Queen Mary College), The Methods of Time Series Analysis
Online books: Paul Söderlind (St. Gallen), Lecture Notes in Financial Econometrics
Online books: A.W. van der Vaart (Vrije U), Time Series