20532  MACROECONOMETRICS
Department of Economics
LUCA SALA
Lezioni della classe erogate in presenza
Prerequisites
Mission & Content Summary
MISSION
CONTENT SUMMARY
 Stationarity.
 Review of ARMA models. Specification and estimation of ARMA.
 Noninvertibilities.
 Nonstationarity.
 Difference stationary vs Trend stationarity.
 Testing for the presence of unit roots: the DickeyFuller 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, BlanchardQuah, 1989 and Gali, 1999 news shocks and noninvertibilities).
 Cointegration (application: King, Plosser, Stock and Watson, 1991).
 Stationarity.
 Review of ARMA models. Specification and estimation of ARMA.
 Noninvertibilities.
 Nonstationarity. Difference stationary vs Trend stationarity.
 Testing for the presence of unit roots: the DickeyFuller 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, BlanchardQuah, 1989 and Gali, 1999, news shocks and noninvertibilities).
 Cointegration (application: King, Plosser, Stock and Watson, 1991).
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
 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 timeseries 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.
APPLYING KNOWLEDGE AND UNDERSTANDING
 Perform empirical analysis to uncover the effects of shocks in the economy.
 Design a wellfunctioning VAR forecasting model.
 Communicate effectively the empirical results of his/her analysis.
 Use a wellknown programming software, Matlab, to perform different kind of timeseries analyses.
 Do empirical analysis in a constructive way and think critically.
Teaching methods
 Facetoface lectures
 Exercises (exercises, database, software etc.)
 Group assignments
DETAILS
The learning experience of this course includes, in addition to facetoface 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  


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ATTENDING STUDENTS
To the end of measuring the acquisition of the abovementioned learning outcomes, the students’ assessment is based on a final written exam. The exam consists of a mix of open questions and applied exercises. Attending students can deliver 4 problem sets. Problem sets teach students the use of Matlab to perform empirical analysis. Successful completion of the problem sets deliver up to 40% of the final grade. The remaining 60% are contributed by the final exam. Alternatively, students who do not wish to hand in the 4 problem sets can take a final written exam (general) that accounts for 100% of the final grad.
NOT ATTENDING STUDENTS
The assessment of nonattending students follows the same rules as the assessment of attending students who do not hand in problem sets: 100% of the grade is set by the performance in the final exam.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
The main course material for both attending and nonattending 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.