Insegnamento a.a. 2022-2023


Department of Economics

Course taught in English
Go to class group/s: 31
FIN (6 credits - I sem. - OP  |  SECS-P/05)
Course Director:

Classes: 31 (I sem.)
Class 31: LUCA SALA

Mission & Content Summary


The empirical analysis of macroeconomic data revolves on the study of time-series data. This course discusses thoroughly the statistical underpinnings of time-series analysis and shows how to apply those concepts to the analysis of the macroeconomy. The course also focuses on the important concept of identification, namely, on how to uncover causal and structural relationships populating economic models but hidden in the data. The course also discusses the most important applications in the literature. In so doing, students should replicate published papers. In the course, students also learn how to program using the software Matlab.


  • Stationarity.
  • Review of ARMA models. Specification and estimation of ARMA.
  • Non-invertibilities.
  • Non-stationarity.
  • Difference stationary vs Trend stationarity.
  • Testing for the presence of unit roots: the Dickey-Fuller test.
  • Spurious regression.
  • Simultaneous equation bias. The problem of identification.
  • The Sims’ critique to old macroeconometrics.
  • VAR models.
  • Granger causality (application: Sims, 1972).
  • Structural VAR and identification (applications: Sims, 1980, Blanchard-Quah, 1989 and Gali, 1999 news shocks and non-invertibilities).
  • Cointegration (application: King, Plosser, Stock and Watson, 1991).
  • Local projections

Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Be familiar with the main concepts and tools of time series analysis and being able to use them in other contexts.
  • Understand a vast majority of the scientific literature on time-series and macroeconometrics.
  • Identify what are the modelling assumptions underlying any structural macroeconometric model.
  • Translate the main assumptions in economic theories into restrictions on the empirical statistical model.


At the end of the course student will be able to...
  • Perform empirical analysis to uncover the effects of shocks in the economy.
  • Design a well-functioning VAR forecasting model.
  • Communicate effectively the empirical results of his/her analysis.
  • Use a well-known programming software, Matlab, to perform different kind of time-series analyses.
  • Do empirical analysis in a constructive way and think critically.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments


The learning experience of this course includes, in addition to face-to-face lectures, a number of classes in the Computer Laboratory, where the software Matlab is introduced. Students hand in 4 problem sets to be solved in groupwork. Problem Sets consist in replicating seminal papers in the literature of Structural VAR. The solution of the Problem Sets is discussed in the Computer Laboratory, where codes and results are shared. Students are encouraged to bring their own views and to share their insights.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • Group assignment (report, exercise, presentation, project work etc.)

Teaching materials


The main course material for both attending and non-attending students is:

  • L. SALA, Lecture note on Time Series Analysis.
  • W. ENDERS, Applied Econometric Time Series, last edition (selected chapters).
  • J.D. HAMILTON, Time Series Analysis, Princeton University Press, 1994 (selected chapters).
  •  Additional references are suggested during the course.
Last change 31/05/2022 12:05