September 13, 2021
Morning session (in presence): 9:00-12:30 (Italian time)
Afternoon session (in presence): 14:00-15:30 (Italian time)
Instructor: Emanuele Bacchiocchi, University of Bologna, Italy
Topic: Stationary and Non-stationary VAR Models; Identification in SVAR Models
The first module is essentially dedicated to representation and estimation of VAR models for stationary time series. The last part of the first module will focus on non-stationary time series and cointegration. The second module, instead, will be dedicated to Structural VAR (SVAR) models with particular emphasis on the identification issue. We will discuss about global and local identification and present different criteria for detecting when structural shocks are locally or globally identified. Finally, we will introduce the idea of set-identification in SVAR models.
References:
– Amisano G. and Giannini C. (1997), Topics in Structural VAR Econometrics, 2nd ed., Springer.
– Giacomini R. and Kitagawa T. (2021), Robust Bayesian Inference for Set-identified Models, Econometrica, forthcoming.
– Kilian L. and Lütkepohl H. (2017), Structural Vector Autoregressive Analysis, Cambridge University Press. (https://sites.google.com/site/lkilian2019/textbook)
– Rubio-Ramirez J. F., Waggoner D. F. and Zha T. (2010), Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference, Review of Economic Studies 77, 665-696.
– Uhlig H. (2005), What are the Effects of Monetary Policy on Output? Results From an Agnostic Identification Procedure, Journal of Monetary Economics 52, 381-419
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September 13, 2021
Afternoon session (online): 16:00-19:00 (Italian time)
Instructor: Lutz Kilian, Federal Reserve Bank of Dallas, US
Topic: VAR Models of the Global Oil Market
This module focuses on structural VAR models of the global oil market, explains the differences across alternative model specifications and reviews recent advances in the econometric methodology for VAR models.
References:
– Kilian L.and Zhou X. (2020), The Econometrics of Oil Market VAR Models, Federal Reserve Bank of Dallas, Working Paper n. 2006.
– 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 Murphy D.P. (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 D.P. Murphy (2014), The Role of Inventories and Speculative Trading in the Global Market for Crude Oil, Journal of Applied Econometrics 29, 454-478.
– Kilian L. (2020), Understanding the Estimation of Oil Demand and Oil Supply Elasticities, Federal Reserve Bank of Dallas, Working Paper n. 2027.
– Inoue A. and Kilian L. (2020), Joint Bayesian Inference about Impulse Responses in VAR Models, Federal Reserve Bank of Dallas, Working Paper n. 2022.
– Kilian L. and Inoue A. (2020), The Role of the Prior in Estimating VAR Models with Sign Restrictions, Federal Reserve Bank of Dallas, Working Paper n. 2030.
– Kilian L. and Lütkepohl H. (2017), Structural Vector Autoregressive Analysis, Cambridge University Press. (https://sites.google.com/site/lkilian2019/textbook)
September 14, 2021
Morning session (in presence): 9:00-12:30 (Italian time)
Afternoon session (in presence) : 14:00-15:30 (Italian time)
Instructor: Andrea Bastianin, University of Milano, Italy
Topic: SVAR Econometrics with Matlab
The first part of this module is an introduction to Matlab as a software for econometric analysis. Empirical applications dealing with estimation and inference for SVAR models will also be covered in the second part of the module.
References:
– Kilian L. and Lütkepohl H. (2017), Structural Vector Autoregressive Analysis, Cambridge University Press. (https://sites.google.com/site/lkilian2019/textbook)
September 14, 2021
Afternoon session (online): 16:00-19:00 (Italian time)
Instructor: Lutz Kilian, Federal Reserve Bank of Dallas, US
Topic: The Transmission of Oil Price Shocks to the Macro Economy
This module focuses on some examples of how to model the transmission of global oil price shocks to the domestic economy.
References:
– Kilian L.and Zhou X. (2020), The Econometrics of Oil Market VAR Models, Federal Reserve Bank of Dallas, Working Paper n. 2006.
– Kilian L. (2009), Comment on ‘Causes and Consequences of the Oil Shock of 2007-08’ by James D. Hamilton, Brookings Papers on Economic Activity 1, 267-278.
– Kilian L. and Park C. (2009), The Impact of Oil Price Shocks on the U.S. Stock Market, International Economic Review 50, 1267-1287.
– Kilian L. and Zhou X. (2020), Oil Prices, Gasoline Prices and Inflation Expectations, Federal Reserve Bank of Dallas, Working Paper n. 2025.
– Kilian L. and Zhou X. (2021), The Propagation of Regional Shocks in Housing Markets: Evidence from Oil Price Shocks in Canada, Journal of Money, Credit and Banking, forthcoming.
September 15, 2021
Morning session (online): 9:00-12:30 (Italian time)
Afternoon session (online): 14:00-18:00 (Italian time)
Instructor: Gazi Salah Uddin, Linköping University, Sweden
Topic: Uncertainty, Volatility, Spillovers and Tail Dependence in Energy Markets
This module offers a comprehensive introduction to uncertainty measures, volatility modelling, spillover approach, cross quantilogram dependence methods, wavelet-based dependence methods and some recent extensions. These models provide very useful statistical econometrics analysis, such as in energy finance and energy economics studies. Course lectures will be complemented by computer laboratory sessions. The lab sessions will be conducted in the open-source R language. An brief introduction to R will be included in the afternoon session.
References:
– Baker S. R., Bloom N. and Davis S. J. (2016), Measuring Economic Policy Uncertainty, The Quarterly Journal of Economics 131, 1593-1636.
– Diebold F.X. and Yilmaz K. (2012), Better to Give Than to Receive: Predictive Directional Measurement of Volatility Spillovers, International Journal of Forecasting 28, 57–66.
– Diebold F.X. and Yilmaz K. (2009), Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets, The Economic Journal 119, 158–171.
– Engle R. (2002), Dynamic Conditional Correlation: a Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business & Economic Statistics 20, 339–350.
– Han H., Linton O., Oka T. and Whang Y.-J. (2016), The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series, Journal of Econometrics 193, 251-270.
September 15, 2021
Aperitif & Social dinner: starting from 19:00 (Italian time)
September 16, 2021
Morning session (in presence): 9:00-12:30 (Italian time)
Afternoon session (in presence): 14:00-18:00 (Italian time)
Instructor: Sébastien Houde, Grenoble Ecole de Management, France and ETH Zürich, Switzerland
Topic: Demand Estimation with Discrete Choices – Endogeneity and Heterogeneity Issues
The goal of this module is to review methods to introduce rich heterogeneity in demand models. Specifically, the focus will be on discrete choice models with unobserved heterogeneity. We will discuss the latest theoretical approaches and applications using Matlab.
References:
Foundations
– Berry S., Levinsohn J. and Pakes A. (1995), Automobile Prices in Market Equilibrium, Econometrica 63, 841-890.
– Nevo A. (2000), A Practitioner’s Guide to Estimation of Random‐coefficients Logit Models of Demand, Journal of Economics & Management Strategy 9, 513-548.
– Dubé J.‐P., Fox J.T. and Su C.-L. (2012), Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation, Econometrica 80, 2231-2267.
– Knittel C.R. and Metaxoglou K. (2014), Estimation of Random-coefficient Demand Models: Two Empiricists’ Perspective, Review of Economics and Statistics 96, 34-59.
Applications
– Li S. (2017), Better Lucky Than Rich? Welfare Analysis of Automobile Licence Allocations in Beijing and Shanghai, The Review of Economic Studies 85, 2389-2428.
Endogeneity
– Bento A.M., Li S. and Roth K. (2012), Is There an Energy Paradox in Fuel Economy? A Note on the Role of Consumer Heterogeneity and Sorting Bias, Economics Letters 115, 44-48.
– Reynaert M. and Verboven F. (2014), Improving the Performance of Random Coefficients Demand Models: the Role of Optimal Instruments, Journal of Econometrics 179, 83-98.
– Armstrong T.B. (2016), Large Market Asymptotics for Differentiated Product Demand Estimators with Economic Models of Supply, Econometrica 84, 1961-1980.
– Grigolon L., Reynaert M. and Verboven F. (2018), Consumer Valuation of Fuel Costs and Tax Policy: Evidence From the European Car Market, American Economic Journal: Economic Policy 10, 193-225.
Heterogeneity
– Berry S., Levinsohn J. and Pakes A. (2004), Differentiated Products Demand Systems From a Combination of Micro and Macro Data: The New Car Market, Journal of Political Economy 112, 68-105.
– Harding M.C. and Hausman J. (2007), Using a Laplace Approximation to Estimate the Random Coefficients Logit Model by Nonlinear Least Squares, International Economic Review 48, 1311-1328.
– Fox J.T., Il Kim K., Ryan S.P., and Bajari P. (2011), A simple Estimator for the Distribution of Random Coefficients, Quantitative Economics 2, 381-418.
– Gillen B.J., Montero S., Moon H.R. and Shum M. (2019), BLP-2LASSO for Aggregate Discrete Choice Models with Rich Covariates, Econometrics Journal 22, 262-281.
September 17, 2021
Morning session (in presence): 9:00-12:30 (Italian time)
Afternoon session (in presence): 14:00-18:00 (Italian time)
Instructor: Matteo Pelagatti, University of Milano-Bicocca, Italy
Topic: Electricity Demand, Prices and Supply Curves: Modelling and Forecasting
The goal of this class is showing how wholesale electricity markets work and how to model equilibrium quantities and prices in these markets. We will concentrate on Unobserved Component Models (UCM), which are a class of time series models particularly fit for modelling data with complex multiple seasonal patterns. We will show how to use sinusoids and cubic splines to build customized seasonal patterns and compare the performance of UCM with few machine learning models. The problem of predicting the whole electricity supply curve in each single auctions will also be addressed. Concepts and methods will be illustrated with applications to real data using the open source software R.
References:
Unobserved component models
– Harvey A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge University Press.
– Durbin J. and Koopman S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press.
– Pelagatti M. (2015), Time Series Modelling with Unobserved Components, Chapman and Hall/CRC.
– Helske J. (2017), KFAS: Exponential Family State Space Models in R, Journal of Statistical Software 78, 1-39.
Econometrics of electricity markets
– Bosco B.P., Parisio L. and Pelagatti M. (2007), Deregulated Wholesale Electricity Prices in Italy, International Advances in Economic Research 13, 415-432.
– Bosco B.P., Parisio L., Pelagatti M. and Baldi F. (2010), Long-run Relations in European Electricity Prices, Journal of Applied Econometrics 25, 805-832.
– Bosco B.P., Parisio L. and Pelagatti M. (2012), Strategic Bidding in Vertically Integrated Power Markets with an Application to the Italian Electricity Auctions. Energy Economics 34, 2046-2057.
– Pelagatti M. (2012), Supply Function Prediction in Electricity Auctions. In: Complex Models and Computational Methods in Statistics, edited by Grigoletto M., Lisi F. and Petrone S., Springer.
– Bosco B.P., Parisio L. and Pelagatti M (2013), Price-capping in Partially Monopolistic Electricity Markets with an Application to Italy, Energy Policy 54, 257-266.
– Gianfreda A., Parisio L. and Pelagatti M. (2016), The Impact of RES in the Italian Day-Ahead and Balancing Markets. The Energy Journal 37, 161-184.
– Gianfreda A., Parisio L. and Pelagatti M. (2016), Revisiting Long-run Relations in Power Markets with High RES Penetration. Energy Policy 94, 432-445.
– Bosco B.P., Parisio L. and Pelagatti M. (2016), Price Coordination in Vertically Integrated Electricity Markets: Theory and Empirical Evidence. The Energy Journal 37, 181-194.
– Lisi F. and Pelagatti M. (2018), Component Estimation for Electricity Market Data: Deterministic or Stochastic? Energy Economics 74, 13-37.
– Gianfreda A., Parisio L. and Pelagatti M. (2018), A Review of Balancing Costs in Italy Before and After RES Introduction. Renewable and Sustainable Energy Reviews 91, 549-563.
– Gianfreda A., Parisio L., Pelagatti M. (2019), The RES-Induced Switching Effect Across Fossil Fuels: An Analysis of Day-Ahead and Balancing Prices, The Energy Journal 40, 365-386.
– Parisio L. and Pelagatti M. (2019), Market Coupling Between Electricity Markets: Theory and Empirical Evidence for the Italian–Slovenian Interconnection, Economia Politica 36, 527-548.