Course 2025-2026 a.y.

20532 - MACROECONOMETRICS

Department of Economics

Course taught in English
31
ACME (6 credits - I sem. - OP  |  SECS-P/05) - AFC (6 credits - I sem. - OP  |  SECS-P/05) - AI (6 credits - I sem. - OP  |  12 credits SECS-P/05) - CLELI (6 credits - I sem. - OP  |  SECS-P/05) - CLMG (6 credits - I sem. - OP  |  SECS-P/05) - DES-ESS (6 credits - I sem. - OP  |  SECS-P/05) - DSBA (6 credits - I sem. - OP  |  SECS-P/05) - EMIT (6 credits - I sem. - OP  |  SECS-P/05) - ESS (6 credits - I sem. - OP  |  SECS-P/05) - FIN (6 credits - I sem. - OP  |  SECS-P/05) - GIO (6 credits - I sem. - OP  |  SECS-P/05) - IM (6 credits - I sem. - OP  |  SECS-P/05) - MM (6 credits - I sem. - OP  |  SECS-P/05) - PPA (6 credits - I sem. - OP  |  SECS-P/05)
Course Director:
LUCA SALA

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


Mission & Content Summary

MISSION

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 macroeconomics. The course also focuses on the important concept of identification, namely, on how to uncover causal and structural relationships present in economic models but hidden in the data. The course also discusses the most important applications in the literature. In so doing, students replicate published papers. In the course, students also learn how to program using the software Matlab.

CONTENT SUMMARY

  • Stationarity and ergodicity.
  • Review of ARMA models. Specification and estimation of ARMA.
  • Non-invertibility.
  • 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; Gali, 1999; news shocks and non-invertibilities).
  • Cointegration (application: King, Plosser, Stock and Watson, 1991).
  • Local projections.
  • Modern IV methods to uncover causality effects.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

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 statistical model.

APPLYING KNOWLEDGE AND UNDERSTANDING

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

  • Lectures
  • Practical Exercises
  • Collaborative Works / Assignments

DETAILS

The learning experience of this course includes, in addition to face-to-face lectures, a number of classes in which the software Matlab is introduced. Students hand in 4 Problem Sets to be solved in groupwork. Problem Sets consist in 1. getting in touch with Matlab; 2. replicating seminal papers in the literature of Structural VAR. The solution of the Problem Sets is discussed in class, 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
  • Oral individual exam
     x
  • Written individual exam (traditional/online)
     x
  • Collaborative Works / Assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

The assessment method is the same for attending and non-attending students.

 

The exam will test the knowledge of students of the theory, the relevant literature and the ability of using the acquired knowledge to set up new empirical analyses. 

 

The final grade will be computed as the maximum of:

1) Grade in the written exam (the grade must be higher than 18)

2) Weighted average of the grade in the written exam (70%, the grade must be higher than 18) and the problem sets grade (30%).

 

The grade obtained in the problem sets will last until September 2026. After that, problem sets will have no value.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

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 02/06/2025 18:20