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Quantitative Macroeconomic Modeling with Structural Vector Autoregressions – An EViews Implementation

by Sam Ouliaris, Adrian Pagan and Jorge Restrepo

Quantitative macroeconomic research is conducted in a number of ways. An important method has been the use of the technique known as Structural Vector Autoregressions (SVARs), which aims to gather information about dynamic processes in macroeconomic systems. This book sets out the theory underlying the SVAR methodology in a relatively simple way and discusses many of the problems that can arise when using the technique. It also proposes solutions that are relatively easy to implement using EViews 9.5. Its orientation is towards applied work and it does this by working with the data sets from some classic SVAR studies.

About The Authors

  •   Sam Ouliaris

  •   Adrian Pagan

  •   Jorge Restrepo

Sam Ouliaris is a Deputy Division Chief in the European and Middle East Division of the IMF’s Institute for Capacity Development (ICD). Prior to joining the IMF’s Institute in 2009, he was a staff member of the IMF’s Research Department (2003–2005), and its Western Hemisphere Department (2005–2009). During 2014-2015, Mr. Ouliaris was Chief of the IMF’s Internal Economics Training Unit, which is responsible for organizing training for IMF staff and visiting country officials.

Dr. Ouliaris holds a Ph.D. in Economics from Yale University specializing in econometrics (time-series analysis, macroeconomic forecasting) and macroeconomics. Before 2011, he was a professor at the National University of Singapore, which he joined in 1988. He has served as a consultant to a number of ministries in Asia, including the Monetary Authority of Singapore, and the Bank Negara Malaysia.

Adrian Pagan is an Emeritus Professor of Economics at the University of Sydney and a Professorial Research Fellow at the University of Melbourne. He has held Professorial appointments at the Australian National University, the University of Rochester, the University of New South Wales and Oxford University.

Professor Pagan is the author of around 150 articles and 4 books. He has been elected to Fellowships of the Econometric Society, the Australian Academy of Social Sciences, the Modelling and Simulation Society of Australia, the Journal of Econometrics, and the Journal of Applied Econometrics. He has been an editor of Econometric Theory and the Journal of Applied Econometrics and an associate editor of Econometrica. He has also co-edited Advanced Texts in Econometrics, (Oxford University Press) and Themes in Modern Econometrics (Cambridge University Press).

In addition to his academic work he has been a Member of the Reserve Bank of Australia Board (1995-2000) and has advised a number of central banks on modeling issues, including the Bank of England, the Norges Bank, the European Central Bank and the Reserve Bank of New Zealand.

Jorge Restrepo is a Senior Economist in the Institute for Capacity Development’s Western Hemisphere Division. Prior to joining ICD in 2010, Dr. Restrepo worked 10 years for the Central Bank of Chile’s Economic Research Department, and also worked at the Central Bank of Colombia. Mr. Restrepo received his Ph.D. in Economics from New York University. His research includes papers on inflation targeting, demand for money, equilibrium unemployment, fiscal rules, and central bank balance sheets.

Download

You may download the full version of the book in PDF form here:
Quantitative Macroeconomic Modeling with Structural Vector Autoregressions (PDF, 5MB / right-click to save)

The authors have also provided a set of companion files containing examples of models and programs. These files are contained in zipped form here:
Example files (ZIP, 7MB / right-click to save)

A description of the files may be found here:
List of files (XLSX, 400KB / right-click to save)


Book Contents

Chapter 1 An Overview of Macro-econometric System Modeling
Chapter 2 Vector Autoregressions: Basic Structure
 
  • Basic Structure
    • Maximum Likelihood Estimation of Basic VARs
    • A Small Macro Model Example
  • Specification of VARs
    • Choosing p
    • Choice of Variables
    • Restricted VARs
    • Augmented VARs
  • Conclusion
Chapter 3 Using and Generalizing a VAR
 
  • Introduction
  • Testing Granger Causality
  • Forecasting using a VAR
    • Forecasting Evaluation
    • Conditional Forecasts
    • Forecasting Using EViews
  • Bayesian VARs
    • The Minnesota Prior
    • Nomral-Wishart Prior
    • Additional Priors Using Dummy Observations or Pseudo Data
    • Forecasting with Bayesian VARs
  • Computing Impulse Responses
  • Standard Errors for Impluse Responses
  • Issues when Using the VAR as a Summative Model
    • Missing Variables
    • Latent Variables
    • Non-Linearities
Chapter 4 Structural Vector Autoregressions with I(0) Processes
 
  • Introduction
  • Mathematical Approaches To Finding Uncorrelated Shocks
  • Generalized Impulse Responses
  • Structural VARs And Uncorrelated Shocks: Representation And Estimation
    • Representation
    • Estimation
    • Maximum Likelihood Estimation
    • Instrumental Variable (IV) Estimation
  • Impulse Responses For An SVAR: Their Construction And Use
    • Construction
    • Variance And Variable Decompositions
  • Restrictions On An SVAR
    • Recursive Systems
    • Imposing Restrictions On The Impact Of Shocks
    • Imposing Restrictions On Parameters - The Blanchard-Perotti Fiscal Policy Model
    • Incorporating Stocks And Flows Plus Identities Into An SVAR - A US Fiscal-Debt Model
    • Treating Exogenous Variables In An SVAR - The SVARX model
    • Restrictions On Parameters And Partial Exogeneity: External Instruments
    • Factor Augmented SVARs
    • Global SVARS (SGVARs)
    • DSGE Models And The Origins of SVARs
  • Standard Errors For Structural Impulse Responses
  • Other Estimation Methods For SVARs
    • Bayesian
    • Using Higher Order Moment Information
Chapter 5 SVARs With I(0) Variables And Sign Restrictions
 
  • Introduction
  • The Simple Structural Models Again And Their Sign Restrictions
  • How Do We Use Sign Restriction Information
    • The SRR Method
    • The SRC Method
    • The SRC And SRR Methods Applied To A Market Model
    • Comparing SRC And SRR With A Simulated Market Model
    • Comparing SRC And SRR With A Small Macro Model And Transitory Shocks
  • What Can And Can't Sign Restrictions Do For You?
    • Sign Restrictions Will Not Give You A Single Model - The Multiple Models Problem
    • Sign Restrictions And the Size of Shocks?
    • Where Do The True Impulse Responses Lie In The Range Of Generaged Models?
    • What Do We Do About Multiple Shocks?
    • What Can Sign Restrictions Do For You?
  • Sign Restrictions In Systems With Block Exogeneity
  • Standard Errors For Sign Restricted Impulses
    • The SRR Method
    • The SRC Method
  • Summary
Chapter 6 Modeling SVARS With Permanent And Transitory Shocks
 
  • Introduction
  • Variables And Shocks
  • Why Can't We Use Transitory Components Of I(1) Variables In SVARs?
  • SVARs With Non-Cointegrated I(1) and I(0) Variables
    • A Two Variables System In I(1) Variables
    • An EViews Application Of The Two I(1) Variable Model
    • An Alternative EViews Application Of The Two I(1) Varialbe Model
    • A Two Variable System With A Permanent And Transitory Shock - The Blanchard and Quah Application In EViews
    • Analytical Solution For The Two Variable Case
    • Revisiting The Small Macro Model With A Permanent Supply Shock
  • Problems With Measuring Uncertainty In Impulse Responses
  • Sign Restrictions When There Are Permanent And Transitory Shocks
Chapter 7 SVARs With Cointegrated And I(0) Variables
 
  • Introduction
  • The VECM And Structural VECM Models
  • SVAR Forms Of The VECM
    • Permament And Transitory Shocks Only
    • Permanent, Transitory And Mixed Shocks
  • Example: Gali's (1999) Technology Shocks and Fluctuations Model
    • Nature Of System And Restrictions Used
    • Estimation Of The System
    • Recovering Impulse Responses To A Single Shock
  • Example: Gali's (1992) IS/LM Model
    • Nature Of System And Restrictions Used
    • Estimation Of The System

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