Summer Institute 2017
Martha J. Bailey, University of Michigan and NBER, Joseph P. Ferrie, Northwestern University and NBER, John M. Abowd, . Census Bureau and NBER Summer Institute 2016
Matching Markets and Market Design
Al Roth - Stanford Unniversity and NBER, Parag Pathak - MIT and NBER, Atila Abdulkadiroglu - Duke University, Nikhil Agarwal - MIT and NBER, and Itai Ashlagi - Stanford University Summer Institute 2015
Lectures on Machine Learning
Susan Athey, Stanford University and NBER and Guido Imbens, Stanford University and NBER Summer Institute 2014
Theory and Application of Network Models
Daron Acemoglu, MIT and NBER and Matthew O. Jackson, Stanford University Summer Institute 2013
Econometric Methods for High-Dimensional Data
Victor Chernozhukov, Massachusetts Institute of Technology, Matthew Gentzkow, University of Chicago and NBER, Christian Hansen, University of Chicago , Jesse Shapiro, University of Chicago and NBER, Matthew Taddy, University of Chicago Summer Institute 2012
Econometric Methods for Demand Estimation
Ariel Pakes, Harvard University and NBER and Aviv Nevo, Northwestern University and NBER Summer Institute 2011
Computational Tools & Macroeconomic Applications
Lawrence Christiano, Northwestern University and NBER and Jesus Fernandez-Villaverde, University of Pennsylvania and NBER Summer Institute 2010
Sydney Ludvigson, New York University and NBER , Yacine Ait-Sahalia, Princeton University and NBER, Michael Brandt, Duke University and NBER and Andrew Lo, MIT and NBER Summer Institute 2009
The Role of Field Experiments in Data Collection
John List, University of Chicago and NBER and Michael Kremer, Harvard University and NBER
Technical Handbook No 4
By Andrew Blake and Haroon Mumtaz
The aim of this handbook is to introduce key topics in Bayesian econometrics from an applied perspective.
The handbook assumes that readers have a fair grasp of basic classical econometrics (. maximum likelihood estimation). It is recommended that readers familiarise themselves with Matlab © programming language to derive the maximum benefit from this handbook. A basic guide to Matlab © is attached at the end of the handbook.
The first chapter of the handbook introduces basic concepts of Bayesian analysis. In particular, the chapter focuses on the technique of Gibbs sampling and applies it to a linear regression model. The chapter shows how to code this algorithm via several practical examples. The second chapter introduces Bayesian vector autoregressions (VARs) and discusses how Gibbs sampling can be used for these models. The third chapter shows how Gibbs sampling can be applied to popular econometric models such as time-varying VARs and dynamic factor models. The final chapter introduces the Metropolis Hastings algorithm. We intend to introduce new topics in revised versions of this handbook on a regular basis.
The handbook comes with a set of Matlab © codes that can be used to replicate the examples in each chapter. The code (provided in ) is organised by chapter. For example, the folder 'Chapter 1' contains all the examples referred to in the first chapter of this handbook.
The views expressed in this handbook are those of the authors, and not necessarily those of the Bank of England. The reference material and computer codes are provided without any guarantee of accuracy.