He has been a research visitor at the Federal Reserve Banks of New York and a visiting scholar at the University of Pennsylvania. He has been a consultant for the UK Treasury the Debt Management Office, and has previously worked in the Monetary Policy Strategy division of the European Central Bank. Bayesian Econometric Methods, second edition, published by Cambridge University Press.Īndrea Carriero is professor of economics at Queen Mary university of London and at the University of Bologna. Bayesian Econometrics, published by Wiley. Non-linear non-Gaussian state space models: The Kim, Shepard, and Chib algorithm for stochastic volatility models. Linear and Gaussian state space models: Forward filtering/backward sampling algorithms. The Generalized Linear Regression Model: Autocorrelation and Heteroskedasticity. The chi-square, gamma, and inverse gamma distributions. The independent Normal-Inverse Gamma prior. Prior selection via the marginal data density. Ridge regression.īayesian estimation of the CLRM: Theil mixed estimator. The Bayesian approach to the classical linear regression model. Introduction: Review of the classical linear regression model. But they also represent the groundwork that underlies popular multivariate macroeconomic models such as Vector Autoregressions (VARs), time-varying parameter VARs (TVP-VARs), factor and Dynamic Stochastic General Equilibirum (DSGE) models. The models and methods covered in this course are of direct use in many macroeconomic applications. These include time series models where parameters change over time, models with autocorrelated disturbances, and stochastic volatility models. Subsequently, the course turns to state space models and discusses estimation of several state space models popularly used in macroeconomics. Computational methods are of great importance in modern Bayesian econometrics, and these are discussed in detail. It begins with a brief introduction to Bayesian econometrics, describing the main concepts underlying Bayesian theory and seeing how Bayesian methods work in the familiar context of the regression model. This is a course in introductory Bayesian econometrics with a focus on models used in empirical macroeconomics. * Laptop required In order to participate in practical sessions, you must bring your own portable computer. Instructor: Christian Brownlees (UPF and BSE) High-Dimensional Time Series Models II: Big Data and Machine Learning. Instructor: Luca Sala (Bocconi University) High-Dimensional Time Series Models I: Factor Models.Instructor: Gabriel Pérez-Quirós (Bank of Spain) Time Series Models for Macroeconomic Analysis II.Instructor: Kristoffer Nimark (Cornell University) Bayesian Time Series Methods III: DSGE Models Estimation.Instructor: Andrea Carriero (Queen Mary University of London and University of Bologna) Bayesian Time Series Methods II: Advanced.Time Series Models for Macroeconomic Analysis I.Instructor: Andrea Carriero (Queen Mary University of London and University of Bologna) Bayesian Time Series Methods I: Introductory.Instructor: Konstantin Boss (UAB and BSE) The level of the courses should be comparable to those taught in the BSE Master's programs. Although econometric theory will have a central role, special attention will be paid to the applications and data. In general, the courses will have an empirical orientation. To survey some of the recent developments in macroeconometrics.To present a variety of empirical applications in macroeconomics.
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