CIDE XXIV Corso Residenziale di Econometria

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1 CIDE XXIV Corso Residenziale di Econometria 8-13 Settembre Palermo Topics in Microeconometrics with Applications to Energy & Environmental Economics 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 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 ( ), as well as in the afternoon ( ). 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 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 September 7, at 18.00, where further organizational details will be given, and material used in the course will be distributed. Coordinators Prof. Matteo Manera, Università di Milano-Bicocca ( matteo.manera@unimib.it) Prof. Fabio Mazzola, Università di Palermo ( fmazzola@unipa.it) Teachers Prof. Barbara Chizzolini, Università Bocconi, Milano ( barbara.chizzolini@unibocconi.it) Prof. Marzio Galeotti, Università di Milano ( marzio.galeotti@unimi.it) Prof. Matteo Manera, Università di Milano-Bicocca ( matteo.manera@unimib.it) 1

2 Course Outline 1 st 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 2 nd and 3 rd part. References Anderson, T.W. and C. Hsiao (1982), Formulation and Estimation of Dynamic Models Using Panel Data, Journal of Econometrics, 18, 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, 2

3 Baltagi, B. (2001), Econometric Analysis of Panel Data, Wiley, 2 nd edition. Blundell, R. and S.R. Bond (1998), Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87, Greene, W. (2000), Econometric Analysis, Prentice Hall, 4 th 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, Kiviet, J.F. (1995), On Bias, Inconsistency, and Efficiency in Various Estimators of Dynamic Panel Data Models, Journal of Econometrics, 68, Manera, M. and M. Galeotti (2005), Microeconometria. Metodi e Applicazioni, Carocci. Nickell, S. (1981), Biases in Dynamic Models with Fixed Effects, Econometrica, 49, nd Part Qualitative and Limited Dependent Variables 1. Models for qualitative dependent variables: binary choices 1.1. Linear probability model 1.2. Logit and Probit models: economic and statistical underpinnings; ML estimation; interpretation of coefficients and marginal effects 1.3. Goodness of fit and prediction 1.4. Logit and Probit models for panel data 2. Models for qualitative dependent variables: multiple choices 2.1. Multinomial and conditional Logit models 2.2. IIA assumption 2.3. Nested Logit models 2.4. Models for ordered choices 3. Models for quantitative, limited dependent variables 3.1. Truncation and censoring 3.2. Tobit models and ML estimation 3.3. Corrected LS 3.4. Models with stochastic thresholds References Amemiya, T. (1981), Qualitative Response Models: a Survey, Journal of Economic Literature, 19, Baltagi, B. (2001), Econometric Analysis of Panel Data, Wiley, 2 nd edition. Chamberlain, G. (1980), Analysis of Covariance with Qualitative Data, Review of Economic 3

4 Studies, 47, Greene, W. (2000), Econometric Analysis, Prentice Hall, 4 th edition. Hayashi, F. (2000), Econometrics, Princeton University Press. Lee, M.J. (2002), Panel Data Econometrics: Methods-of-Moments and Limited Dependent Variables, Academic Press. Maddala, G.S. (1983), Limited Dependent and Qualitative Variables in Econometrics, Cambridge University Press. Manera, M. and M. Galeotti (2005), Microeconometria. Metodi e Applicazioni, Carocci. Peracchi, F. (2001), Econometrics, Wiley. Robinson, P.M. (1982), On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables, Econometrica, 50, Tobin, J. (1958), Estimation of Relationships for Limited Dependent Variables, Econometrica, 26, Verbeek, M. (2000), A Guide to Modern Econometrics, Wiley. Wooldridge, J.M. (2002), Econometric Analysis of Cross Section and Panel Data, The MIT Press. 3 rd 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, 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, 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, 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, Galeotti, M., A. Lanza and F. Pauli (2006), Reassessing the Environmental Kuznets Curve for CO2 Emissions: a Robustness Exercise, Ecological Economics, 57, Galeotti, M., A. Lanza and M.C.L. Piccoli (2010), The Demographic Transition and the Ecological 4

5 Transition: Enriching the Environmental Kuznets Curve Hypothesis, mimeo. Griffin, J.M. (1991), Methodological Advances in Energy Modelling: , Energy Journal, 14, Griffin, J. (1985), OPEC Behavior: A Test of Alternative Hypotheses, American Economic Review, 75, Pindyck, R.S. (1979), Interfuel Substitution and the Industrial Demand for Energy: An International Comparison, Review of Economics and Statistics, 61, Pireddu, G. and S.D Ascenzo (1996), I modelli di scelta aleatoria: Metodologia e applicazione al caso della scelta del sistema di riscaldamento, Economia delle fonti di energia e dell ambiente, 39, Popp D. (2002), Induced Innovation and Energy Prices, American Economic Review, 92, 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, Stevens, P. (2000), The Economics of Energy 1, Journal of Energy Literature, 6, Stevens, P. (2001), The Economics of Energy 2, Journal of Energy Literature, 7, Vaage, K. (2000), Heating Technology and Energy Use: A Discrete Continuous Choice Approach to Norwegian Household Energy Demand, Energy Economics, 22, 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,

6 Course Timetable Sunday, 7 September Hotel Ibis Style, h.18.00: Welcome meeting Monday, 8 September Camera di Commercio, h : Presentation of the course - Camera di Commercio, h : Stationary Panel Data Models (lecture) - Camera di Commercio, h : Stationary Panel data Models (lecture and class) Tuesday, 9 September Camera di Commercio, h : Stationary Panel Data Models (lecture) - Camera di Commercio, h : Stationary Panel Data Models (lecture and class) Wednesday, 10 September Camera di Commercio, h : Qualitative and Limited Dependent Variables (lecture) - Camera di Commercio, h : Qualitative and Limited Dependent Variables (lecture and class) Thursday, 11 September Camera di Commercio, h : Qualitative and Limited Dependent Variables (lecture) - Camera di Commercio, h : Qualitative and Limited Dependent Variables (lecture and class) Friday, 12 September Camera di Commercio, h : Energy & Environmental Economic Modelling (lecture) - Camera di Commercio, h : Energy & Environmental Economic Modelling (lecture) Saturday, 13 September Camera di Commercio, h : Energy & Environmental Economic Modelling (class) - Camera di Commercio, h : Diploma awards delivery and final comments 6