Topics we will cover
  • Vector autoregressions:
    –motivation
    –Estimation, MLE, OLS, Bayesian using analytical and Gibbs 
    Sampling MCMC methods
    –Identification [short run restrictions, long run restrictions, 
    sign restrictions, max share criteria]
    – interpretation, use, contribution to macroeconomics
  • Factor models in vector autoregressions
  • TVP VAR estimation using kernels.
  • Bootstrapping
      VARs  useful sources
  • Chris Sims, 'Macroeonomics and reality‘
  • Lutz Kilian 'Structural Vector Autoregressions‘
  • Fabio Canova:  Methods for Applied Business C
   ycle research
  • James Hamilton 'Time Series Analysis‘
  • Helmut Luktepohl
    'New introduction to multiple time series analy
   sis'
       Useful sources, ctd
  • Stock and Watson:  implications of dynamic fac
   tor models for VAR analysis
  • Stock and Watson:  'Dynamic factor models'
    Matrix/linear algebra pre-requisites
  • Scalar, vector, matrix.
  • Transpose
  • Inverse (matrix equivalent of dividing).
  • Diagonal matrix.
  • Eigenvalues and eigenvectors.
  • Powers of a matrix.
  • Matrix series sums.  Matrix equivalent of geometric scalar sums.
  • Variance-covariance matrix.
  • Cholesky factor of a variance-covariance matrix.
  • Givens matrix.
        Some applications
  • Christiano, Eichenbaum, Evans:  
   ‘Monetary policy shocks:  what have we learned 
   and to what end?’
  • Christiano, Eichenbaum and Evans (2005):  ‘Nominal 
   rigidities and the dynamics effects of a monetary policy 
   shock’
  • Mountford, Uhlig (2008):  
   ‘what are the effects of fiscal policy shocks?’
  • Gali (1999):  ’Technology, employment and the business 
   cycle….’
  • I’ll remind you of these as we go through.