Prof. Andrea Cipollini
University of Palermo
fax: +39 091 423900 ; phone: +39 091 23867514

Prof. Matteo Manera
University of Milano-Bicocca
fax: +39 02 52036915; phone: +39 02 64485819

Please contact us at for further information.



Prof. Andrea Cipollini, University of Palermo

Prof. Marzio Galeotti, University of Milano

 Prof. Matteo Manera, University of Milano-Bicocca

Course outline, objectives and learning outcomes

Energy economics and environmental economics are nowadays well established disciplines which have gained a relevant role in terms of volume and quality of published research, as well as number of dedicated courses at undergraduate and postgraduate levels, both in Europe and all over the world. The scientific literature in the fields of energy and environmental economics is dominated by empirical contributions, which use different econometrics techniques to investigate several issues, such as the dynamic behavior of energy prices and volatilities, the dynamic structure of the international oil market, the relationship between pollution, income and population across different countries and time periods. The aim of the course is to provide students with an overview, both methodological and applied, of what is currently referred to as the “econometrics of energy and environment”, that is a thoughtful selection of the most important econometric techniques which should be mastered by any researcher willing to contribute to those fields. The course is divided in three parts, which share the interchange between theory and empirical applications with popular econometric software. In the first part, models for panel data are theoretically discussed and empirically implemented with real data on the relationship between carbon dioxide, gross domestic product and population for a set of OECD countries. The second part is devoted to the methodological presentation of Structural VAR (SVAR) models, as well as to the empirical analysis of the international oil market. The last part of the course discusses, both theoretically and empirically, the most recent approaches to energy demand estimation, the representation of the asymmetric relation between energy prices and the so-called Environmental Kuznets Curve (EKC) hypothesis.  At the end of the course, participants will be able to critically evaluate the empirical literature on energy and environmental economics, to handle panel data and SVAR models and to estimate and test their own relations of interest: energy demand models, the inter-links among the price of oil, aggregate demand and supply, asymmetries between input and output prices, the EKC hypothesis.

Entry requirements

Basic knowledge of econometrics to a level comparable with the content of a standard undergraduate course in econometrics.  Examples of references are:

- Greene, W.H. (2008), Econometric Analysis, 6th edition, Pearson Prentice-Hall, Chapters 1-8, 12.

 - Manera, M. and M. Galeotti (2005), Microeconometria. Metodi e Applicazioni, Carocci, Chapter 1.

Schedule of the course:

1st part - Stationary Panel Data Models

1. Models for pooled time series

1.1. System estimation: SURE

1.2. Model with individual heteroskedasticity and correlation  1.3. Model with individual heteroskedasticity and serial correlation  1.4. Model with individual heteroskedasticity, serial and individual correlation

2. Models for longitudinal data  

2.1. Fixed effects model: Within estimator and test for fixed effects  2.2. Random effects model: GLS/FGLS estimator, Between estimator, computation of individual effects and test for random effects  2.3. Random effects correlated with regressors

3. Models with instrumental variables and two-way models  

3.1. Consistent and efficient IV estimators  3.2. Testing the absence of correlation between individual effects and regressors 3.3. Two-way models

4. Dynamic panel data models

 4.1. Inconsistency of LS estimators 4.2. The Anderson-Hsiao approach  4.3. The Arellano-Bond approach  4.4. Exogenous regressors  4.5. Autocorrelation and specification tests  4.6. GMM estimation and parameter restrictions

Classes will use the software STATA. The same software will be used in the applications of the 3rd part of the course. Lab exercises will use time series and cross-sectional data on oil and fuel prices, carbon dioxide emissions, gross domestic product and population.

2nd part - Structural Form VAR

1. Identification

1.1. Reduced form and structural VAR 1.2. Structural model identification through zero exclusion restrictions 1.3. Identification through sign restrictions: an introduction  1.4. Identification through heteroskedasticity: an introduction

2. Estimation

 2.1. Log-likelihood estimation of reduced form and structural VAR   2.2. Vector Moving Average representation  2.3. Impulse response analysis; variance and historical decompositions

Classes will use the software R. Lab exercises will use Kilian’s data on oil price, oil production and proxy of real economic activity (all data are available from the Journal of Applied Econometrics data archive). Lab exercises will focus on zero exclusion restrictions, beyond lag selection, diagnostic checking and stationarity tests.

3rd part - Energy & Environmental Economic Modelling

1. Environment, growth and population: the Environmental Kuznets Curve hypothesis  2. Household energy demand: a discrete choice approach 3. The relationship between oil and gasoline prices: country differences and asymmetries< br/> 4. Innovation and diffusion in energy technologies


Reference textbooks for the course

1st part:

 Baltagi, B. (2001), Econometric Analysis of Panel Data, Wiley, 2nd edition.  

Greene, W. (2000), Econometric Analysis, Prentice Hall, 4th edition.

 Manera, M. and M. Galeotti (2005), Microeconometria. Metodi e Applicazioni, Carocci.

2nd part:

Kilian, L. (2009), “Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market”, American Economic Review, 99, 1053–1069.

Lütkepohl H. and A. Netsunajev (2014), “Disentangling demand and supply shocks in the crude oil market: how to check sign restrictions in Structural VARS”, Journal of Applied Econometrics, 29, 479–496.  

Rigobon, R. (2003), “Identification through heteroskedasticity”, The Review of Economics and Statistics, 85, 777–792.

3rd part:

 Adeyemi, O.I. and L.C. Hunt (2007), “Modeling OECD industrial energy demand: asymmetric price responses and energy-saving technical change”, Energy Economics, 29, 693-709.

Galeotti, M., A. Lanza and M. Manera (2009), “On the robustness of the robustness checks on the Environmental Kuznets Curve”, Environmental and Resource Economics, 42, 551-574.

Stevens, P. (2000), “The economics of energy 1”, Journal of Energy Literature, 6, 3-31.

Stevens, P. (2001), “The economics of energy 2”, Journal of Energy Literature, 7, 3-45.


Students requiring accommodation will stay at the Hotel Ibis Style (via F. Crispi, 230, Palermo). The hotel is located in the city center, close to the Stazione Marittima. Buses from the airport stop very close to the hotel (bus stop “Porto”, via E. Amari). Breakfast for residential participants will be served in the penthouse of the Hotel Ibis Style, while lunch for residents and non-residential participants will be offered in a typical restaurant very close to the Camera di Commercio.


Lectures and classes will be held in the Camera di Commercio (via E. Amari 11, at walking distance from the Hotel Ibis Style), in the morning (9.00-13.00) and afternoon (14.30-18.00). To obtain maximum benefit from practical computer session, it is essential that participants are equipped with their own computers. Participants are kindly requested to gather in the hall of the Hotel Ibis Style on Sunday, 3 September, at 18.00, where further organizational details will be given, and material used in the course will be distributed.


Fees and Enrollment

  •  Students, PhD students and temporary university staff: 600€ 
  •  University staff: 700€ 
  •  Others: 2300€

Fee includes: accommodation (usually in double room) with breakfast and lunch starting from Monday.

Partecipants who wish to attend two or three modules, are allowed the following reduced fees per Module

  •  Students, PhD students and temporary university staff: 500€ per Module
  •  University staff: 600€ per Module
  •  Others: 1750€ per Module