90388 - ADVANCED PANEL DATA METHODS

Conoscenze e abilità da conseguire

At the end of the course, students know the most appropriate estimating techniques for dynamic panel data models, both microeconomic (large and with more than one cross-sectional dimension) and macroeconomic (over a long time span). Specifically, they can: - critically understand theoretical and applied aspects of the vast literature based on dynamic panel data models; - apply dynamic panel data models techniques to their own analyses by programming specific routines using the STATA software.

Contenuti

Nowadays panel datasets, whether longitudinal, cross-sectional time-series data (CSTS) or multilevel data with observations at higher and lower levels, permeate empirical research on many topics, from classical economics to behavioural and political economics. The aim of the course is to provide an overview, both methodological and applicative, of econometric models for panel data, where observations are available in at least two dimensions. Throughout the course, to facilitate understanding and introduce important topics, N will denote individuals (cross-sections) and T will denote time periods (time-series). The first part of the course deals with micro panel data ( N > T). After a brief recap of the main strengths and weaknesses of the various estimation methods in static models under the assumptions of classical exogeneity of the explanatory variables and endogeneity due to correlation with individual heterogeneity, standard endogeneity (correlation with idiosyncratic shocks) will also be introduced. We will move on to the analysis of dynamic models and the Generalised Method of Moments (GMM), a milestone in advanced panel model estimation. When T>N or N and T are of similar size, the main issues will be non-stationarity and cointegration, analysed and discussed in the light of parameter heterogeneity and correlated common effects.

Testi/Bibliografia

The material (articles, notes, programmes, data-sets, further references) will be distributed during the lectures and made available on the Virtuale platform.

As a refresh, students are invited to have a look at:
Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, 7th Edition, Cengage;
Verbeek M. 2017 A guide to Modern Econometrics, Wiley, 5th Edition.

The main reference books are Wooldridge J. M. 2010 Econometric Analysis of Cross-Section and Panel Data, 2nd ed, Cambridge Mass.: MIT Press; Arellano, M. (2003) Panel Data Econometrics, Oxford University Press; Baltagi B. H. (2021) Econometric Analysis of Panel Data, 5^th ed., Springer International Publishing; Hsiao, C. (2014) Analysis of Panel Data, 3^rd ed., Cambridge University Press.

For Erasmus students: be aware that the course requires some basic prerequisites.

Have a look at this page, specifically to the content of ECONOMETRIC METHODS, MICROECONOMETRICS (FOR N>T) AND MACROECONOMETRICS (FOR N<T)

32682 Econometrics, A (I.C.)
B2153 ECONOMETRIC METHODS
B2154 MICROECONOMETRICS

Econometrics, B (I.C.)
B2153 ECONOMETRIC METHODS
B2155 MACROECONOMETRICS

Metodi didattici

At each step of the course, the methodologies will be accompanied by hands-on empirical applications with an econometric software (Stata available using the CAMPUS license and students' university credentials). At the end of the course, participants will be able to critically evaluate the empirical literature based on panel data, and to model and estimate their own issue of interest, according to the problems at hand: static versus dynamic approaches, heterogeneity and clustering, exogeneity versus endogeneity of covariates, GMM, unit roots and long/short run relationships.

Modalità di verifica e valutazione dell'apprendimento

During the course, students will be provided with homeworks to carry out (alone or in groups) according to the deadlines provided by the lecturer. These are empirical analyses based on what will have been seen in class, datasets and research questions will be provided by the lecturer. Any homework done and handed in to the lecturer by the deadline will award additional points for the final assessment. The final examination, which is compulsory, involves the presentation and in-class discussion of an empirical analysis carried out on the basis of a research question provided by the lecturer.
Non-attending students will have to take a written examination on theoretical and applied topics, with open questions designed to assess their ability to understand model specifications and estimated results, as well as to evaluate the strengths and weaknesses of alternative estimation methods.
To take the final examination, students must register on Almaesami.

Evaluation of the home and class assignments:
3/28-30L: the write-ups reveal a proficient and exhaustive knowledge of the issue, and an excellent ability of comprehension and analytical execution.
2/24-27: from the write-ups it emerges an appreciable degree of knowledge of the issue, and a good ability of comprehension and analytical execution.
1/18-23: the write-ups are untidy, with theoretical and methodological inaccuracies.
0/<18: wrong or not delivered write-ups.

Strumenti a supporto della didattica

Theoretical lectures are associated with working sessions; during them students will receive the suggestions needed to run their own empirical analysis. The data-sets and the programming files to perfom applied analyses will be provided during the lectures. The distributed material (articles, notes, programs, and data-sets) will be make available on the Virtuale platform.

STATA software HERE

Link ad altre eventuali informazioni

https://sites.google.com/site/mariaelenabontempi/home/teaching/Advanced_Panel_Data_Methods

Orario di ricevimento

Consulta il sito web di Maria Elena Bontempi