Micro-Econometrics, Causal Inference and Time Series Econometrics
A.Y. 2020/2021
Learning objectives
The aim of this course is twofold. First, to learn how to analyse time-series (typically macro-) data. In particular, how to identify the effect of past shocks on the current state of the world, how to forecast future values and how to model the dynamic interaction between different series.
Second, to analyse the main challenges faced by economists and social scientists in answering empirical questions using micro‐data. The main emphasis will be on learning how to establish causal relationships between different variables and how to use this evidence to inform policy makers' decisions.
Second, to analyse the main challenges faced by economists and social scientists in answering empirical questions using micro‐data. The main emphasis will be on learning how to establish causal relationships between different variables and how to use this evidence to inform policy makers' decisions.
Expected learning outcomes
By the end of the course students will be able to:
Understand the difference between a time series and an independent random sample.
Apply non-parametric and parametric techniques to model time series.
Choose and estimate parametric models for time series.
Compute the impulse response function.
Forecast future values.
Handle real‐world data.
Identify causal effects using micro-data
Link econometric theory with data work and produce an insightful and coherent empirical analysis.
Understand the difference between a time series and an independent random sample.
Apply non-parametric and parametric techniques to model time series.
Choose and estimate parametric models for time series.
Compute the impulse response function.
Forecast future values.
Handle real‐world data.
Identify causal effects using micro-data
Link econometric theory with data work and produce an insightful and coherent empirical analysis.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Organization of teaching
The lectures will take place on the Microsoft Teams platform. It will be possible to follow these in real time as per the schedule in the timetable. It will also be possible to access the recordings on the same platform.
The modality and criteria to take part to the lectures in person, that may require a booking in advance with a specific app, will be advertised in advance on ARIEL. Additional information regarding updates in response to the development of the Covid-19 pandemic will also be advsertised on the same site.
Syllabus and teaching material
The syllabus and teaching materials are not affected.
Assessment
Exams from distance will take place using the platform exam.net. A complete guideline to the assessment is available on ARIEL.
The lectures will take place on the Microsoft Teams platform. It will be possible to follow these in real time as per the schedule in the timetable. It will also be possible to access the recordings on the same platform.
The modality and criteria to take part to the lectures in person, that may require a booking in advance with a specific app, will be advertised in advance on ARIEL. Additional information regarding updates in response to the development of the Covid-19 pandemic will also be advsertised on the same site.
Syllabus and teaching material
The syllabus and teaching materials are not affected.
Assessment
Exams from distance will take place using the platform exam.net. A complete guideline to the assessment is available on ARIEL.
Prerequisites for admission
There is no formal prerequisite.
This course requires knowledge of inferential statistic and of matrix algebra.
Some previous knwoledge of econonometrics is recommended for the micro-econometrics and causal inference module.
This course requires knowledge of inferential statistic and of matrix algebra.
Some previous knwoledge of econonometrics is recommended for the micro-econometrics and causal inference module.
Assessment methods and Criteria
One written exam for Time Series.
- Microeconometrics and causal inference
Only for the first session of finals students will be provided the opportunity to handle a project paper on the part of casual inference and one for micro-econometrics. The topic can be freely selected by the student. The focus of the paper needs to be an application of one of the approaches discussed during the lectures.
After the first session of finals, the chance to handle a paper will no longer be available and the student needs to take a written text.
- Microeconometrics and causal inference
Only for the first session of finals students will be provided the opportunity to handle a project paper on the part of casual inference and one for micro-econometrics. The topic can be freely selected by the student. The focus of the paper needs to be an application of one of the approaches discussed during the lectures.
After the first session of finals, the chance to handle a paper will no longer be available and the student needs to take a written text.
Module Micro-econometrics and Causal Inference
Course syllabus
At the end of the module students should be able to handle and interpret the results of empirical analyses both from a statistical and economic perspective.
Microeconometrics (Check regularly ARIEL for a detailed and updated syllabus)
1. Ordinary Least Squares regression: recap and issues related with the violations of the classical assumptions
2. Instrumental variables estimation
3. Introduction to panel data econometrics
4. Binary outcome and count data models
Causal Inference (Check regularly ARIEL for a detailed and updated syllabus)
1. Challenges when disentangling causation from correlation: recap and main pitfalls of the methods analysed so far
2. Difference-in-differences estimation
3. Regression discontinuities design estimation
4. Challenges and strength of Randomized Controlled Trials
Microeconometrics (Check regularly ARIEL for a detailed and updated syllabus)
1. Ordinary Least Squares regression: recap and issues related with the violations of the classical assumptions
2. Instrumental variables estimation
3. Introduction to panel data econometrics
4. Binary outcome and count data models
Causal Inference (Check regularly ARIEL for a detailed and updated syllabus)
1. Challenges when disentangling causation from correlation: recap and main pitfalls of the methods analysed so far
2. Difference-in-differences estimation
3. Regression discontinuities design estimation
4. Challenges and strength of Randomized Controlled Trials
Teaching methods
Lectures and tutorial using the Stata software. For campus licenses see: https://work.unimi.it/servizi/servizi_tec/121946.htm
Teaching Resources
· Lecture notes
· For microeconometrics students can choose - depending on their background - one of the following textbooks:
o [Advanced Level] A. Colin Cameron and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge University Press, New York, May 2005
o [Introductory Level] M. Verbeek A Guide to Modern Econometrics, 5th Edition
· For causal inference slides and material will be based on:
o J. D. Angrist and J. Pischke. Most Harmless Econometrics: An Empiricist's Companion (2009)
o J. D. Angrist and J. Pischke. Mastering 'Metrics: The Path from Cause to Effect (2015)
· Papers replicated during tutorials (see ARIEL for details)
· Stata sessions are part of the program
· For microeconometrics students can choose - depending on their background - one of the following textbooks:
o [Advanced Level] A. Colin Cameron and Pravin K. Trivedi. Microeconometrics: methods and applications. Cambridge University Press, New York, May 2005
o [Introductory Level] M. Verbeek A Guide to Modern Econometrics, 5th Edition
· For causal inference slides and material will be based on:
o J. D. Angrist and J. Pischke. Most Harmless Econometrics: An Empiricist's Companion (2009)
o J. D. Angrist and J. Pischke. Mastering 'Metrics: The Path from Cause to Effect (2015)
· Papers replicated during tutorials (see ARIEL for details)
· Stata sessions are part of the program
Module Time Series Econometrics
Course syllabus
Topics will include.
Definition of univariate time series.
Non-parametrics characterisation.
Ergodicity and stationarity as generalisation of the iid framework.
Parametric modelling of weakly dependent univariate time series (ARMA modelling). Impulse response functions for ARMA.
Inference in ARMA modelling.
Forecasting with ARMA models.
Model selection: parsimonious modelling.
Parametric modelling of strongly dependent univariate time series: unit root modelling. Forecasting with unit roots.
Unit root testing.
Multivariate modelling: VARMA and VAR modelling for weakly autocorrelated time series. Impulse response function for VARs.
Identification and estimation in VAR models.
Regression models for weakly dependent and unit root multivariate time series (cointegration).
Definition of univariate time series.
Non-parametrics characterisation.
Ergodicity and stationarity as generalisation of the iid framework.
Parametric modelling of weakly dependent univariate time series (ARMA modelling). Impulse response functions for ARMA.
Inference in ARMA modelling.
Forecasting with ARMA models.
Model selection: parsimonious modelling.
Parametric modelling of strongly dependent univariate time series: unit root modelling. Forecasting with unit roots.
Unit root testing.
Multivariate modelling: VARMA and VAR modelling for weakly autocorrelated time series. Impulse response function for VARs.
Identification and estimation in VAR models.
Regression models for weakly dependent and unit root multivariate time series (cointegration).
Teaching methods
20 two-hours lectures (40 hours)
Teaching Resources
The main reference textbook for the course is:
Time Series Analysis, by J. D. Hamilton, 1994, Princeton University Press .
Other references may be mentioned as the course progresses.
Time Series Analysis, by J. D. Hamilton, 1994, Princeton University Press .
Other references may be mentioned as the course progresses.
Module Micro-econometrics and Causal Inference
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professors:
Bastianin Andrea, Grembi Veronica
Module Time Series Econometrics
SECS-P/05 - ECONOMETRICS - University credits: 6
Lessons: 40 hours
Professor:
Iacone Fabrizio
Professor(s)
Reception:
Wednesday, 11AM to 1PM. Please email me to arrange an appointment
Stanza 4 (Second floor)