SIdE - Italian Econometric Society

SIdE - Italian Econometric Society
Postgraduate courses
4-9 September 2017 - Palermo
Topics in Energy and Environmental Econometrics
General Information
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). The hotel has a very convenient
wi-fi system, which allows participants (25 at maximum), who own a personal computer, to
access their e-mail accounts, to receive class material and to communicate with teachers and
coordinators. On this respect, and in order to obtain maximum benefit from each practical
computer session, it is essential that participants are equipped with their own computers.
Lectures and classes will be held in the nearby Camera di Commercio, which is located at
walking distance from the hotel (via E. Amari 11). Lectures and classes will be scheduled in the
morning (9.00-13.00), as well as in the afternoon (14.30-18.00). 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 at 13.30 in a typical restaurant very close to the
Camera di Commercio. 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.
Coordinators
Prof. Andrea Cipollini, University of Palermo (e-mail: [email protected])
Prof. Matteo Manera, University of Milano-Bicocca (e-mail: [email protected])
Teachers
Prof. Andrea Cipollini, University of Palermo (e-mail: [email protected])
Prof. Marzio Galeotti, University of Milano (e-mail: [email protected])
Prof. Matteo Manera, University of Milano-Bicocca (e-mail: [email protected])
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Course Outline
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.
References
Anderson, T.W. and C. Hsiao (1982), “Formulation and estimation of dynamic models using
panel data”, Journal of Econometrics, 18, 67-82.
Arellano, M. and S. R. Bond (1991), “Some specification tests for panel data: Monte Carlo
evidence and an application to employment equations”, Review of Economic Studies, 58,
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277-298.
Baltagi, B. (2001), Econometric Analysis of Panel Data, Wiley, 2nd edition.
Blundell, R. and S.R. Bond (1998), “Initial conditions and moment restrictions in dynamic panel
data models”, Journal of Econometrics, 87, 115-144.
Greene, W. (2000), Econometric Analysis, Prentice Hall, 4th edition.
Hahn, J. and G. Kuersteiner (2002), “Asymptotically unbiased inference for a dynamic panel
model with fixed effects when both N and T are large”, Econometrica, 70, 1639-1659.
Kiviet, J.F. (1995), “On bias, inconsistency, and efficiency in various estimators of dynamic panel
data models”, Journal of Econometrics, 68, 53-78.
Manera, M. and M. Galeotti (2005), Microeconometria. Metodi e Applicazioni, Carocci.
Nickell, S. (1981), “Biases in dynamic models with fixed effects”, Econometrica, 49, 1399-1416.
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.
References
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.
Kilian L. and D. Murphy (2012), “Why agnostic sign restrictions are not enough: understanding
the dynamics of oil market VAR models”, Journal of the European Economic Association,
10, 1166–1188.
Kilian L. and C. Park (2009), “The impact of oil price shocks on the U.S. stock market”,
International Economic Review, 50, 1267–1287.
Lanne M. and H. Lütkepohl (2008), “Identifying monetary policy shocks via changes in
volatility”, Journal of Money, Credit, and Banking, 40, 1131–1149.
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
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Statistics, 85, 777–792.
Rigobon, R. and B. Sack (2003), “Measuring the reaction of monetary policy to the stock
market”, The Quarterly Journal of Economics, 118, 639–669.
Slides on Structural VAR by A. Cesa Bianchi.
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
4. Innovation and Diffusion in Energy Technologies
References
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.
Balestra, P. and M.Nerlove (1966), “Pooling cross section and time series data in the estimation
of a dynamic model: the demand for natural gas”, Econometrica, 34, 585-612.
Berndt, E.R., C.J. Morrison and G.C.Watkins (1981), “Dynamic models of energy demand: an
assessment and comparison”, in E.R. Berndt and B.C. Field (eds.), Modeling and Measuring
Natural Resource Substitution, MIT Press, 259-289.
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.
Galeotti, M., A. Lanza and F. Pauli (2006), “Reassessing the Environmental Kuznets Curve for
CO2 emissions: a robustness exercise”, Ecological Economics, 57, 152-163.
Galeotti, M., A. Lanza and M.C.L. Piccoli (2010), “The demographic transition and the ecological
transition: enriching the Environmental Kuznets Curve hypothesis”, mimeo.
Griffin, J.M. (1991), “Methodological advances in energy modelling: 1970-1990”, The Energy
Journal, 14, 111-124.
Griffin, J. (1985), “OPEC behavior: a test of alternative hypotheses”, American Economic Review,
75, 954-963.
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Pindyck, R.S. (1979), “Interfuel substitution and the industrial demand for energy: an
international comparison”, Review of Economics and Statistics, 61, 259-268.
Popp D. (2002), “Induced innovation and energy prices”, American Economic Review, 92, 160180.
Ramcharran, H. (2002), “Oil production responses to price changes: an empirical application of
the competitive model to OPEC and non-OPEC countries”, Energy Economics, 24, 97-106.
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.
Verdolini, E. and M. Galeotti (2011), “At home and abroad: an empirical analysis of innovation
and diffusion in energy technologies”, Journal of Environmental Economics and
Management, 61, 119–134.
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Course Timetable
Sunday, 3 September 2017
- Hotel Ibis Style, h.18.00: Welcome meeting
Monday, 4 September 2017
- Camera di Commercio, h. 9.00-9.15: Presentation of the course
- Camera di Commercio, h. 9.15-13.00: Stationary Panel Data Models (lecture)
- Camera di Commercio, h. 14.30-18.00: Stationary Panel data Models (lecture and class)
Tuesday, 5 September 2017
- Camera di Commercio, h. 9.00-13.00: Stationary Panel Data Models (lecture)
- Camera di Commercio, h. 14.30-18.00: Stationary Panel Data Models (lecture and class)
Wednesday, 6 September 2017
- Camera di Commercio, h. 9.00-13.00: Structural form VAR (lecture)
- Camera di Commercio, h.14.30-18.00: Structural form VAR (lecture and class)
Thursday, 7 September 2017
- Camera di Commercio, h. 9.00-13.00: Structural form VAR (lecture)
- Camera di Commercio, h.14.30-18.00: Structural form VAR (lecture and class)
Friday, 8 September 2017
- Camera di Commercio, h. 10.00-13.00: Energy & Environmental Economic Modelling (lecture)
- Camera di Commercio, h.14.30-18.00: Energy & Environmental Economic Modelling (lecture)
Saturday, 9 September 2017
- Camera di Commercio, h. 9.00-12.00: Energy & Environmental Economic Modelling (class)
- Camera di Commercio, h. 12.00-12.15: Diploma awards delivery and final comments
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