1Solving, estimating and evaluating DSGE modelsHumboltd University, March 2014

Fabio CanovaEUI and CEPR

OutlineThe course presents a self-contained exposition of methods needed to solve and estimateDSGE models and undertake policy analysis.The lectures for this course will based on chapters 2, 7 and 9 and 11 of my book: Methods

for Applied Macroeconomic Research, Princeton University, Press, 2007.

ProgramMonday March 3, 2014. First and second order approximations to the solution of DSGEmodels.Tuesday, March 4, 2014. Maximum likelihood estimation of DSGE modelsWednesday, March 5, 2014, Introduction bayesian estimation and posterior simulators.Thursday, March 6, 2014, Bayesian estimation of DSGEsFriday, March 7, 2014, Forecasting and evaluation of DSGE models.

Reading list

1) Solution of DSGE models and approximation methods

Dynamic programming and Lagrangian multipliers approach. Perturbation methods: linear and second order approximations. Dynare vs. other programs.

References

Cooley, T., 1995, (ed.) Frontiers of Business Cycle Research , Princeton University Press. Marimon R. and Scott, A. ,1999, (eds.) Computational Methods for the Study of DynamicEconomies, Oxford University Press.

King, R., Plosser, C. and Rebelo, S., 2002, Appendix to Production, Growth and BusinessCycle: I The Basic Neoclassical models, Journal of Computational Economics, 20, 87-116.

2 Klein, P., 2000, Using the generalized Schur form to solve a multivariate linear rationalexpectations model, Journal of Economic Dynamics and Control, 24, 1405-1423.

Uhlig, H., 1999, A methods for Analyzing Nonlinear Dynamic Stochastic Models Easilyin Marimon, R. and Scott A. (eds.) Computational Methods for the Study of DynamicEconomies, Oxford University Press.

Schmitt-Grohe, S. and Uribe, M. 2004, Solving Dynamic General Equilibrium Models usingSecond Order Approximation to the Policy function, Journal of Economic Dynamics andControl, 28, 755-775

Kim, J., Kim S., Schaumburg, E. and Sims, C., 2008, Calculating and using Second OrderAccurate Solutions of Discrete Time Dynamic Equilibrium Models, Journal of EconomicDynamics and Control, 32, 3397-3414.

Dynare manual, 2013, available on line at http://www.cepremap.cnrs.fr/dynare/ Den Haan, W., 2009, Perturbation methods, manuscript, available at http://www1.feb.uva.nl/mint/wdenhaan/notes.htm.

2) Maximum likelihood estimation

State space models and Kalman lter Prediction error decomposition and numerical tips Frequency domain maximum likelihood Application to DSGE models

References

Hamilton, J., 1994, Time Series Models, Princeton University Press. Hansen, L. and Sargent, T., 1998 , Recursive linear Models of Dynamic Economies , PrincetonUniversity Press.

Altug, S., 1989, Time to build and Aggregate Fluctuations: Some New Evidence, Interna-tional Economic Review, 30, 883-920.

Kim, J., 2000, Constructing and Estimating a realistic Optimizing Model of Monetary Policy,Journal of Monetary Economics, 45, 329-359

Ireland, P., 2000, Sticky Price Models and the Business Cycle: Specication and Stability,Journal of Monetary Economics, 47, 3-18.

3 Ireland, P., 2004, A method for taking Models to the data, Journal of Economic Dynamicsand Control, 28, 1205-1226.

Linde, J. , 2005, Estimating New Keynesian Phillips curve: A Full Information maximumlikelihood, Journal of Monetary Economics, 52, 1125-1159.

Canova, F. and Menz, T., 2011, The role of money in propagating business cycles: an inter-national investigation, Journal of Money Credit and Banking,43, 577-609

3-4) Bayesian methods, posterior simulators and application to DSGE

Preliminaries : Bayes Theorem, Prior Selection. Normal approximations MCMC methods (Gibbs sampler and Metropolis-Hastings). Bayesian DSGE models

References

Bauwens, L., M. Lubrano and J.F. Richard, 1999, Bayesian Inference in Dynamics Econo-metric Models , Oxford University Press.

Gelman, A., J. B. Carlin, H.S. Stern and D.B. Rubin, 1995, Bayesian Data Analysis, Chap-man and Hall, London.

Robert, C. and Casella, G. , 2003, Monte Carlo Statistical Methods, Springer Verlag. Canova, F. and Pappa, E., 2007, Price Dierential in Monetary Union: the role of scalshocks, Economic Journal, 117, 717-737.

Casella, G. and George, E., 1992, Explaining the Gibbs Sampler, The American Statistician,46, 167-174.

Chib, S. and Greenberg, E., 1995, Understanding the Hasting-Metropolis Algorithm, TheAmerican Statistician, 49, 327-335.

Geweke, J., 1995, Monte Carlo Simulation and Numerical Integration in Amman, H., Kendrick,D. and Rust, J. (eds.) Handbook of Computational Economics, Amsterdaam, North Holland,731-800.

Tierney, L., 1994, Markov Chains for Exploring Posterior Distributions (with discussion),Annals of Statistics, 22, 1701-1762.

An, S and Schorfheide, F. ,2007, Bayesian analysis of DSGE models, Econometric Reviews,26, 113-172 (with discussion).

4 Schorfheide, F, 2011 Estimation and Evaluation of DSGE models: Progress and challenges,NBER working paper 16781.

Dri ll, J, Pesaran, H. Smith, R. G. Ascari, M. Miller, R. Werner (2011) The future ofmacroeconomics, Manchester Journal, supplement, 1-38. (4 articles and an introduction).

Fernandez Villaverde, J., 2009, The econometrics of DSGE models, NBER working paper14677.

Primiceri, G. and Justianiano, A., 2008, The time varying volatility of Macroeconomic Fluc-tuations, American Economic Review, 98, 604-641.

Smets, F. and R. Wouters, 2003, An Estimated Stochastic DSGE model of the Euro Area,Journal of the European Economic Association, 5, 1123-1175.

Smets, F. and R. Wouters, 2007, Shocks and Frictions in US Business cycles, AmericanEconomic Review, 97, 586-606.

Schorfheide, F., 2000 Loss function based evaluation of DSGE models, Journal of AppliedEconometrics, 15, 645-670.

5) Forecasting and evaluation of DSGE models

Topics in DSGE estimation Evaluating DSGE models and policy analyses

References

Adolfson, M, Laseen, S., Linde, J. and Villani, M., 2008, Evaluating an Estimated newKeynesian small open economy model, Journal of Economic Dynamics and Control, 32,2690-2721.

Adolfson, M, Linde, J., 2008, Parameter Identication in an Estimated New Keynesian OpenEconomy Model, Federal Reserve Board, manuscript.

Canova, F. and Sala, L., 2009, Back to square one: Identication issues in DSGE models,Journal of Monetary Economics, 56(4), 431-449.

Del Negro, M, Schorfheide, F., Smets, F. and Wouters, R., 2006, On the t of New-keynesianmodels, Journal of Business and Economic Statistics, 25, 143-162.

Iskrev, N., 2010, Local identication in DSGE models, Journal of Monetary Economics, 57,189-202.

5 Canova, F. and Paustian, M., 2011, Business cycle measurement with some theory, Journalof Monetary Economics, 48, 365-381.

Chari, V., Kehoe, P. and McGratten, E., 2009, New Keynesian models: not yet useful forpolicy analysis, American Economic Journal: Macroeconomics, 1, 242-266.

Canova, F., 2012, Bridging DSGEmodels and the data, available at http://www.crei.cat/people/canova. Canova, F., and Ferroni, F., 2011, Multiple ltering devices for the estimation of DSGEmodels, Quantitative Economics, 2, 73-98.