YADA is program for conducting Bayesian estimation and evaluation of Dynamic Stochastic General Equilibrium (DSGE) and Vector AutoRegressive (VAR) models. It is developed by the New Area-Wide Model (NAWM) team at the Forecasting and Policy Modelling Division (formerly at the Monetary Policy Research Division and before that at the Econometric Modelling Division within the Directorate General Research) of the European Central Bank (ECB). Unlike other DSGE estimation applications, such as Dynare, YADA is a GUI-based program.
To take a tour of YADA I warmly recommend that you check out the YADA online help. Among other things, you can look at a large number of screenshots.
- Download YADA as matlab code, version 4.30, released on September 21, 2017 (12,437,351 bytes).
- Download the YADA Manual in pdf, dated September 21, 2017 (2,576,681 bytes).
- Download the guide to extending YADA in pdf, dated September 15, 2017 (860,910 bytes)
NOTE: The minimum system requirements for running YADA are:
- MS-Windows operating system with Matlab version 5.3 or later. For Matlab versions prior to 7, make sure that it is permitted to run the file aimparser.exe, located in the bin directory of YADA; or
- UNIX or Macintosh OS X with Matlab version 7 or later.
YADA is licensed under the GNU General Public License and is copyright © 2006-2017 European Central Bank.
YADA is distributed with nine examples that allow you to start playing with DSGE models directly. The examples are given by the models studied by:
- An, S. and Schorfheide, F. (2007), "Bayesian Analysis of DSGE Models", Econometric Reviews, 26, 113-172.
- Lubik, T.A. and Schorfheide, F. (2007), "Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation", Journal of Monetary Economics, 54, 1069-1087.
- Smets, F. and Wouters, R. (2007), "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach", American Economic Review, 97, 586-606.
- Fagan, G., Lothian, J.R., and McNelis, P. (2013), "Was the Gold Standard Really Destabilizing" Journal of Applied Econometrics, 28, 231-249.
- GalÝ, J., Smets, F., and Wouters, R. (2012), "Unemployment in an Estimated New Keynesian Model", in D. Acemoglu and M. Woodford (Editors), NBER Macroeconomics Annual 2011, 329-360, University of Chicago Press. The euro area version of this model from Smets, Warne and Wouters (2014) is included in the YADA distribution.
- Del Negro, M. and Schorfheide, F. (2013), "DSGE Model-Based Forecasting":, in G. Elliott and A. Timmermann (Editors), Handbook of Economic Forecasting, volume 2, 57-140, North Holland: Amsterdam.
- Small-scale version of the Smets and Wouters (2007) model in Del Negro and Schorfheide (2013). Actual US data is included.
- Herbst, E. and F. Schorfheide (2016), Bayesian Estimation of DSGE Models, Manuscript, Princeton University Press: Princeton. More information about the book is available from Frank Schorfheide's homepage. The YADA-based model is the same as the in An and Schorfheide example, but here it has actual US data available online from FRED at the Federal Reserve Bank of St Louis. The construction of the data is described in Appendix B of the book by Herbst and Schorfheide.
- Leeper, E.M., Plante, M., and Traum, N. (2010), "Dynamics of Fiscal Financing in the United States", Journal of Econometrics, 156, 304-321.
- YADA has been developed in connection with the New Area-Wide Model (NAWM) project at the ECB. A working paper describing the NAWM (Christoffel, Coenen and Warne, 2008) is available for download from the website of the ECB, while a working paper describing forecasting with DSGE models (Christoffel, Coenen and Warne, 2010) is also available for download from the same site.
- YADA relies on some external code. In particular, it is uses some functions and ideas developed by Mattias Villani at Link÷ping University. Moreover, it uses AiM (developed by Anderson and Moore at the Federal Reserve) to parse and (optionally) solve the DSGE model. It also includes csminwel (for optimization) and gensys (for solving a DSGE model), developed by Christopher Sims. Moreover, it includes code from Stixbox, developed by Anders Holtsberg, the Lightspeed Toolbox by Tom Minka, the Kernel Density Estimation Toolbox by Christian Beardah, and from Dynare by Michel Juillard and Stephane Adjemian.
- One of YADA'a model solvers uses the Klein (2000) QZ (generalized Schur) decomposition based algorithm. The code in YADA has been inspired by Paul Klein's own solab code. The latter code is available from his website for Matlab as well as for Gauss and Fortran.
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Last Updated: September 21, 2017