SPRING COURSES 

WEEK 1
March 26-March 29, 2018
Practical DSGE Models
Fabio Canova (Norwegian Business School)

Topics covered

  • Bayesian estimation of DSGE models: a refresher. Identification issues.
  • Choosing the variables to estimate DSGE models. Dealing with trends/nonbalanced growth paths. Data Rich DSGEs,
  • The role of measurement error, singularity and estimation.
  • Eliciting databased priors and  prior  predictive  analysis.
  • Higher order  solution, model pruning and estimation
  • Time varying DSGE models: structural  breaks, occasionally binding constraints, continuously  varying  parameters,  Markov  switching.
  • Dealing with model misspecification, evaluation and respecification.
  • Composite  likelihood  and  quasi-Bayesian approaches.

WEEK 2 
April 3-April 6, 2018
New Keynesiam  models  with  search and  matching frictions, financial  frictions and banks. Transmission and  policies.
Paolo Gelain (Cleveland Fed)  
Francesco Furlanetto (Norges Bank)

Topics covered

  • A basic medium scale New Keynesian model: the transmission mechanism of shocks, monetary policy rules and inflation targeting, discussion of the model-based output gap and monetary policy trade-offs, optimal monetary policy,  two-agent New Keynesian (TANK) models, comparison with VAR empirical evidence.
  • Extension I, Introducing unemployment: sticky wages, nominal rigidities and to search and matching frictions, the natural rate of unemployment and shifts in the Beveridge curve, optimal monetary policy.
  • Extension II, Introducing financial frictions: the financial accelerator mechanism, monetary policy trade offs in the presence of financial frictions, collateral constraints, the role of financial factors in macroeconomic fluctuations, monetary policy and leaning against the wind.
  • Extension III, Introducing  Banks: Collateral constraints,  moral  harard models, amplification effects,  monetary  and prudential  policies, the  role  of  capital requirements, global  banks.

SUMMER COURSES 

WEEK 3
July 23-27, 2018
Econometric techniques for large data sets
Dimitris Korobilis (University of Essex)

Topics covered

  • Univariate classical inferencial procedures with many predictors (extreme bounds, dynamic  factors, forecast combinations)
  • Univariate Bayesian  approaches with  many  predictors (basic methods, Srinkage  priors, Bayesian  model averaging)
  • Multivariate inference (Bayesian VARs, FAVAR, Large Bayesian VARs).
  • Machine Learning (ML) algorithms (Supervised learning, Bayesian deep learning, General strategies for inference, Distributed computation)
  • Machine Learning (ML) algorithms for high-dimensional inference in regression and VAR  models (Random projections, Random forests
  • Scalable approximations, Belief propagation and message passing algorithms)

WEEK 4
July 30 –August 3, 2018
Macroeconomics at the Zero Lower Bound
Mathias Trabandt (Freie Universitat Berlin)

Topics covered

  • The Canonical New-Keynesian model with  search and matching and  financial frictions.
  • Effects of fiscal policy in normal times vs. deep recessions (zero or effective lower bound).
  • The fiscal multiplier in the linearized vs. fully nonlinear New-Keynesian model.
  • Importance of real rigidities for the multiplier: Kimball vs. Dixit-Stiglitz aggregators.
  • Assessing self-financing fiscal stimulus and self-defeating fiscal consolidations.
  • Solution techniques for  models  with frictions and constraints.

Application deadline: March 10 for the Spring courses, July 10 for the Summer courses.

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