Course 2019-2020 a.y.

20203 - ECONOMETRICS

Department of Economics
Course taught in English
Go to class group/s: 20 - 21
DES-ESS (8 credits - II sem. - OB  |  SECS-P/05)
Course Director:
GIOVANNI BRUNO

Classes: 20 (II sem.) - 21 (II sem.)
Instructors:
Class 20: GIOVANNI BRUNO, Class 21: GIOVANNI BRUNO


Class-group lessons delivered  on campus

Suggested background knowledge

For a fruitful and effective learning experience, it is strongly recommended a preliminary knowledge in basic calculus, probability theory and linear algebra.

Mission & Content Summary

MISSION

The course offers an introduction to a variety of econometric methods and models, focusing on the basic theory and some more advanced results. In the second part, there is a focus on econometric methods for macroeconomic and financial variables. The course is completed by a set of applications based on simulated and actual data, implemented using STATA and Eviews.

CONTENT SUMMARY

  • Finite simple properties of the OSL estimator in the classical regression model.
  • Asymptotic properties of estimators and tests in the presence of possible endogeneity.
  • Error heteroskedasticity and serial correlation.
  • Panel data models.
  • Univariate and multivariate time series models.
  • Forecast evaluation, comparison and combination.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Define the key elements of an econometric analysis.
  • Recognize the proper data and estimation method to be used.
  • Explain the outcome of the estimation and testing.
  • Select the most appropriate econometric model.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Interpret the results of advanced empirical analyses using cross-sectional, time series and panel data.
  • Compare the outcome of alternative estimation and testing procedures.
  • Evaluate the validity of the assumptions underlying specific econometric methods.
  • Predict the future evolution of economic variables.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)

DETAILS

  • Solution of theoretical questions related to the various topics in the syllabus.
  • Empirical applications based on simulated and actual economic data.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x

ATTENDING AND NOT ATTENDING STUDENTS

Written exam(s) with open ended questions aimed at assessing whether the students are capable of:

  • Defining the key elements of an econometric analysis.
  • Recognizing the proper data and estimation method to be used.
  • Interpreting the results of empirical analyses using both cross sectional and time series data.
  • Comparing the outcome of alternative estimation and testing procedures.
  • Evaluating the validity of the assumptions underlying specific econometric methods.
  • Using the models for predicting the future evolution of economic variables.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • E. GHYSELS, M. MARCELLINO, Applied Economic Forecasting, Oxford University Press, 2018. 
  • J.M. WOOLDRIGE, Introductory Econometrics: A Modern Approach, South-Western College Pub, 2013, 5th edition.
  • Additional required texts are communicated at the beginning of the course and additional material is posted on Bboard.
Last change 04/06/2019 17:42