diff --git a/doc/manual/source/bibliography.rst b/doc/manual/source/bibliography.rst index dce3c0d89..a8442cbca 100644 --- a/doc/manual/source/bibliography.rst +++ b/doc/manual/source/bibliography.rst @@ -44,6 +44,7 @@ Bibliography * Kim, Jinill and Sunghyun Kim (2003): “Spurious welfare reversals in international business cycle models,” *Journal of International Economics*, 60, 471–500. * Kanzow, Christian and Stefania Petra (2004): “On a semismooth least squares formulation of complementarity problems with gap reduction,” *Optimization Methods and Software*, 19, 507–525. * Kim, Jinill, Sunghyun Kim, Ernst Schaumburg, and Christopher A. Sims (2008): “Calculating and using second-order accurate solutions of discrete time dynamic equilibrium models,” *Journal of Economic Dynamics and Control*, 32(11), 3397–3414. +* Komunjer, Ivana and Ng, Serena (2011): ”Dynamic identification of dynamic stochastic general equilibrium models”, *Econometrica*, 79, 1995–2032. * Koop, Gary (2003), *Bayesian Econometrics*, John Wiley & Sons. * Koopman, S. J. and J. Durbin (2000): “Fast Filtering and Smoothing for Multivariate State Space Models,” *Journal of Time Series Analysis*, 21(3), 281–296. * Koopman, S. J. and J. Durbin (2003): “Filtering and Smoothing of State Vector for Diffuse State Space Models,” *Journal of Time Series Analysis*, 24(1), 85–98. @@ -52,13 +53,16 @@ Bibliography * Liu, Jane and Mike West (2001): “Combined parameter and state estimation in simulation-based filtering”, in *Sequential Monte Carlo Methods in Practice*, Eds. Doucet, Freitas and Gordon, Springer Verlag. * Lubik, Thomas and Frank Schorfheide (2007): “Do Central Banks Respond to Exchange Rate Movements? A Structural Investigation,” *Journal of Monetary Economics*, 54(4), 1069–1087. * Murray, Lawrence M., Emlyn M. Jones and John Parslow (2013): “On Disturbance State-Space Models and the Particle Marginal Metropolis-Hastings Sampler”, *SIAM/ASA Journal on Uncertainty Quantification*, 1, 494–521. +* Mutschler, Willi (2015): “Identification of DSGE models - The effect of higher-order approximation and pruning“, *Journal of Economic Dynamics & Control*, 56, 34-54. * Pearlman, Joseph, David Currie, and Paul Levine (1986): “Rational expectations models with partial information,” *Economic Modelling*, 3(2), 90–105. * Planas, Christophe, Marco Ratto and Alessandro Rossi (2015): “Slice sampling in Bayesian estimation of DSGE models”. * Pfeifer, Johannes (2013): “A Guide to Specifying Observation Equations for the Estimation of DSGE Models”. * Pfeifer, Johannes (2014): “An Introduction to Graphs in Dynare”. +* Qu, Zhongjun and Tkachenko, Denis (2012): “Identification and frequency domain quasi-maximum likelihood estimation of linearized dynamic stochastic general equilibrium models“, *Quantitative Economics*, 3, 95–132. * Rabanal, Pau and Juan Rubio-Ramirez (2003): “Comparing New Keynesian Models of the Business Cycle: A Bayesian Approach,” Federal Reserve of Atlanta, *Working Paper Series*, 2003-30. * Raftery, Adrian E. and Steven Lewis (1992): “How many iterations in the Gibbs sampler?,” in *Bayesian Statistics, Vol. 4*, ed. J.O. Berger, J.M. Bernardo, A.P. * Dawid, and A.F.M. Smith, Clarendon Press: Oxford, pp. 763-773. * Ratto, Marco (2008): “Analysing DSGE models with global sensitivity analysis”, *Computational Economics*, 31, 115–139. +* Ratto, Marco and Iskrev, Nikolay (2011): “Identification Analysis of DSGE Models with DYNARE.“, *MONFISPOL* 225149. * Schorfheide, Frank (2000): “Loss Function-based evaluation of DSGE models,” *Journal of Applied Econometrics*, 15(6), 645–670. * Schmitt-Grohé, Stephanie and Martin Uríbe (2004): “Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function,” *Journal of Economic Dynamics and Control*, 28(4), 755–775. * Schnabel, Robert B. and Elizabeth Eskow (1990): “A new modified Cholesky algorithm,” *SIAM Journal of Scientific and Statistical Computing*, 11, 1136–1158. diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst index 096ebbf34..e465161e5 100644 --- a/doc/manual/source/the-model-file.rst +++ b/doc/manual/source/the-model-file.rst @@ -9154,15 +9154,27 @@ Performing identification analysis * minimal system as in *Komunjer and Ng (2011)* * reduced-form solution and linear rational expectation model as in *Ratto and Iskrev (2011)* + Note that for orders 2 and 3, all identification checks are based on the pruned + state space system as in *Mutschler (2015)*. That is, theoretical moments and + spectrum are computed from the pruned ABCD-system, whereas the minimal system + criteria is based on the first-order system, but augmented by the theoretical + (pruned) mean at order 2 or 3. - 2. Identification strength analysis based on sample information matrix as in - *Ratto and Iskrev (2011)* + 2. Identification strength analysis based on (theoretical or simulated) curvature of + moment information matrix as in *Ratto and Iskrev (2011)* 3. Parameter checks based on nullspace and multicorrelation coefficients to determine which (combinations of) parameters are involved *General Options* + .. option:: order = 1|2|3 + + Order of approximation. At orders 2 and 3 identification is based on the + pruned state space system. Note that the order set in other functions does + not overwrite the default. + Default: ``1``. + .. option:: parameter_set = OPTION See :opt:`parameter_set ` for @@ -9220,13 +9232,15 @@ Performing identification analysis * ``0``: efficient sylvester equation method to compute analytical derivatives * ``1``: kronecker products method to compute analytical - derivatives + derivatives (only at order=1) * ``-1``: numerical two-sided finite difference method - to compute all identification Jacobians + to compute all identification Jacobians (numerical tolerance + level is equal to ``options_.dynatol.x``) * ``-2``: numerical two-sided finite difference method to compute derivatives of steady state and dynamic model numerically, the identification Jacobians are - then computed analytically + then computed analytically (numerical tolerance + level is equal to ``options_.dynatol.x``) Default: ``0``. @@ -9297,7 +9311,7 @@ Performing identification analysis .. option:: no_identification_spectrum Disables computations of identification check based on - Qu and Tkachenko (2012)'s G, i.e. Gram matrix of derivatives of + *Qu and Tkachenko (2012)*'s G, i.e. Gram matrix of derivatives of first moment plus outer product of derivatives of spectral density. .. option:: grid_nbr = INTEGER @@ -9311,7 +9325,7 @@ Performing identification analysis .. option:: no_identification_minimal Disables computations of identification check based on - Komunjer and Ng (2011)'s D, i.e. minimal state space system + *Komunjer and Ng (2011)*'s D, i.e. minimal state space system and observational equivalent spectral density transformations. *Misc Options*