Verified Commit 49cba343 authored by Willi Mutschler's avatar Willi Mutschler Committed by Stéphane Adjemian
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📖 Fix references in documentation

parent c24445e8
......@@ -12,6 +12,7 @@ Bibliography
* Andrews, Donald W.K (1991): “Heteroskedasticity and autocorrelation consistent covariance matrix estimation”, *Econometrica*, 59(3), 817–858.
* Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992): “International Real Business Cycles,” *Journal of Political Economy*, 100(4), 745–775.
* Baxter, Marianne and Robert G. King (1999): “Measuring Business Cycles: Approximate Band-pass Filters for Economic Time Series,” *Review of Economics and Statistics*, 81(4), 575–593.
* Born, Benjamin and Johannes Pfeifer (2014): “Policy risk and the business cycle”, *Journal of Monetary Economics*, 68, 68-85.
* Boucekkine, Raouf (1995): “An alternative methodology for solving nonlinear forward-looking models,” *Journal of Economic Dynamics and Control*, 19, 711–734.
* Brooks, Stephen P., and Andrew Gelman (1998): “General methods for monitoring convergence of iterative simulations,” *Journal of Computational and Graphical Statistics*, 7, pp. 434–455.
* Cardoso, Margarida F., R. L. Salcedo and S. Feyo de Azevedo (1996): “The simplex simulated annealing approach to continuous non-linear optimization,” *Computers & Chemical Engineering*, 20(9), 1065-1080.
......@@ -21,19 +22,21 @@ Bibliography
* Collard, Fabrice (2001): “Stochastic simulations with Dynare: A practical guide”.
* Collard, Fabrice and Michel Juillard (2001a): “Accuracy of stochastic perturbation methods: The case of asset pricing models,” *Journal of Economic Dynamics and Control*, 25, 979–999.
* Collard, Fabrice and Michel Juillard (2001b): “A Higher-Order Taylor Expansion Approach to Simulation of Stochastic Forward-Looking Models with an Application to a Non-Linear Phillips Curve,” *Computational Economics*, 17, 125–139.
* Corona, Angelo, M. Marchesi, Claudio Martini, and Sandro Ridella (1987): “Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm”, *ACM Transactions on Mathematical Software*, 13(3), 262–280.
* Del Negro, Marco and Franck Schorfheide (2004): “Priors from General Equilibrium Models for VARs”, *International Economic Review*, 45(2), 643–673.
* Corana, Angelo, M. Marchesi, Claudio Martini, and Sandro Ridella (1987): “Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm”, *ACM Transactions on Mathematical Software*, 13(3), 262–280.
* Del Negro, Marco and Frank Schorfheide (2004): “Priors from General Equilibrium Models for VARs”, *International Economic Review*, 45(2), 643–673.
* Dennis, Richard (2007): “Optimal Policy In Rational Expectations Models: New Solution Algorithms”, *Macroeconomic Dynamics*, 11(1), 31–55.
* Duffie, Darrel and Kenneth J. Singleton (1993): “Simulated Moments Estimation of Markov Models of Asset Prices”, *Econometrica*, 61(4), 929-952.
* Durbin, J. and S. J. Koopman (2012), *Time Series Analysis by State Space Methods*, Second Revised Edition, Oxford University Press.
* Fair, Ray and John Taylor (1983): “Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectation Models,” *Econometrica*, 51, 1169–1185.
* Fernández-Villaverde, Jesús (2010): “The econometrics of DSGE models,” *SERIEs*, 1, 3–49.
* Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2004): “Comparing Dynamic Equilibrium Economies to Data: A Bayesian Approach,” *Journal of Econometrics*, 123, 153–187.
* Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2005): “Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,” *Journal of Applied Econometrics*, 20, 891–910.
* Fernández-Villaverde, Jesús (2010): “The econometrics of DSGE models,” *SERIEs*, 1, 3–49.
* Ferris, Michael C. and Todd S. Munson (1999): “Interfaces to PATH 3.0: Design, Implementation and Usage”, *Computational Optimization and Applications*, 12(1), 207–227.
* Geweke, John (1992): “Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments,” in J.O. Berger, J.M. Bernardo, A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of the Fourth Valencia International Meeting on Bayesian Statistics, pp. 169–194, Oxford University Press.
* Geweke, John (1999): “Using simulation methods for Bayesian econometric models: Inference, development and communication,” *Econometric Reviews*, 18(1), 1–73.
* Giordani, Paolo, Michael Pitt, and Robert Kohn (2011): “Bayesian Inference for Time Series State Space Models” in: *The Oxford Handbook of Bayesian Econometrics*, ed. by John Geweke, Gary Koop, and Herman van Dijk, Oxford University Press, 61–124.
* Goffe, William L., Gary D. Ferrier, and John Rogers (1994): “Global Optimization of Statistical Functions with Simulated Annealing,” *Journal of Econometrics*, 60(1/2), 65–100.
* Hansen, Lars P. (1982): “Large sample properties of generalized method of moments estimators,” Econometrica, 50(4), 1029–1054.
* Hansen, Nikolaus and Stefan Kern (2004): “Evaluating the CMA Evolution Strategy on Multimodal Test Functions”. In: *Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII*, Proceedings, Berlin: Springer, 282–291.
* Harvey, Andrew C. and Garry D.A. Phillips (1979): “Maximum likelihood estimation of regression models with autoregressive-moving average disturbances,” *Biometrika*, 66(1), 49–58.
* Herbst, Edward (2015): “Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models,” *Computational Economics*, 45(4), 693–705.
......@@ -41,8 +44,8 @@ Bibliography
* Iskrev, Nikolay (2010): “Local identification in DSGE models,” *Journal of Monetary Economics*, 57(2), 189–202.
* Judd, Kenneth (1996): “Approximation, Perturbation, and Projection Methods in Economic Analysis”, in *Handbook of Computational Economics*, ed. by Hans Amman, David Kendrick, and John Rust, North Holland Press, 511–585.
* Juillard, Michel (1996): “Dynare: A program for the resolution and simulation of dynamic models with forward variables through the use of a relaxation algorithm,” CEPREMAP, *Couverture Orange*, 9602.
* 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 and Sunghyun Kim (2003): “Spurious welfare reversals in international business cycle models,” *Journal of International Economics*, 60, 471–500.
* 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.
......@@ -51,25 +54,26 @@ Bibliography
* Kuntsevich, Alexei V. and Franz Kappel (1997): “SolvOpt - The solver for local nonlinear optimization problems (version 1.1, Matlab, C, FORTRAN)”, University of Graz, Graz, Austria.
* Laffargue, Jean-Pierre (1990): “Résolution d’un modèle macroéconomique avec anticipations rationnelles”, *Annales d’Économie et Statistique*, 17, 97–119.
* 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.
* Mutschler, Willi (2018): “Higher-order statistics for DSGE models”, *Econometrics and Statistics*, 6(C), 44-56.
* 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.
* Rabanal, Pau and Juan Rubio-Ramírez (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.
* Ruge-Murcia, Francisco J. (2012): “Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles“, *Journal of Economic Dynamics and Control*, 36, 914-938.
* 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.
* Schorfheide, Frank (2000): “Loss Function-based evaluation of DSGE models,” *Journal of Applied Econometrics*, 15(6), 645–670.
* Sims, Christopher A., Daniel F. Waggoner and Tao Zha (2008): “Methods for inference in large multiple-equation Markov-switching models,” *Journal of Econometrics*, 146, 255–274.
* Skoeld, Martin and Gareth O. Roberts (2003): “Density Estimation for the Metropolis-Hastings Algorithm,” *Scandinavian Journal of Statistics*, 30, 699–718.
* Smets, Frank and Rafael Wouters (2003): “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area,” *Journal of the European Economic Association*, 1(5), 1123–1175.
* Stock, James H. and Mark W. Watson (1999). “Forecasting Inflation,”, *Journal of Monetary Economics*, 44(2), 293–335.
* Uhlig, Harald (2001): “A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily,” in *Computational Methods for the Study of Dynamic Economies*, Eds. Ramon Marimon and Andrew Scott, Oxford University Press, 30–61.
* U.S. Census Bureau (2017): “X-13 ARIMA-SEATSReference Manual”.
* U.S. Census Bureau (2017): “X-13 ARIMA-SEATS Reference Manual”.
* Villemot, Sébastien (2011): “Solving rational expectations models at first order: what Dynare does,” *Dynare Working Papers*, 2, CEPREMAP.
......@@ -3621,8 +3621,8 @@ corresponding to a random draw of the shocks.
The main algorithm for solving stochastic models relies on a Taylor
approximation, up to third order, of the expectation functions (see
*Judd (1996)*, *Collard and Juillard (2001a)*, *Collard and Juillard
(2001b)*, and *Schmitt-Grohé and Uríbe (2004)*). The details of the
*Judd (1996)*, *Collard and Juillard (2001a, 2001b)*, and
*Schmitt-Grohé and Uríbe (2004)*). The details of the
Dynare implementation of the first order solution are given in
*Villemot (2011)*. Such a solution is computed using the
``stoch_simul`` command.
......@@ -4630,7 +4630,8 @@ Estimation based on likelihood
Provided that you have observations on some endogenous variables, it
is possible to use Dynare to estimate some or all parameters. Both
maximum likelihood (as in *Ireland (2004)*) and Bayesian techniques
(as in *Rabanal and Rubio-Ramirez (2003)*, *Schorfheide (2000)* or
(as in *Fernández-Villaverde and Rubio-Ramírez (2004)*,
*Rabanal and Rubio-Ramirez (2003)*, *Schorfheide (2000)* or
*Smets and Wouters (2003)*) are available. Using Bayesian methods, it
is possible to estimate DSGE models, VAR models, or a combination of
the two techniques called DSGE-VAR.
......@@ -4947,7 +4948,7 @@ block decomposition of the model (see :opt:`block`).
* Posterior mean and highest posterior density interval (shortest
credible set) from posterior simulation
* Convergence diagnostic table when only one MCM chain is used or
Metropolis-Hastings convergence graphs documented in *Pfeiffer
Metropolis-Hastings convergence graphs documented in *Pfeifer
(2014)* in case of multiple MCM chains
* Table with numerical inefficiency factors of the MCMC
* Graphs with prior, posterior, and mode
......@@ -6554,8 +6555,8 @@ block decomposition of the model (see :opt:`block`).
Order of approximation around the deterministic steady
state. When greater than 1, the likelihood is evaluated with a
particle or nonlinear filter (see *Fernandez-Villaverde and
Rubio-Ramirez (2005)*). Default is ``1``, i.e. the likelihood
particle or nonlinear filter (see *Fernández-Villaverde and
Rubio-Ramírez (2005)*). Default is ``1``, i.e. the likelihood
of the linearized model is evaluated using a standard Kalman
filter.
......
......@@ -15,7 +15,7 @@ class and methods for dates. Below, you will first find the class and
methods used for creating and dealing with dates and then the class
used for using time series. Dynare also provides an interface to the
X-13 ARIMA-SEATS seasonal adjustment program produced, distributed, and
maintained by the US Census Bureau.
maintained by the US Census Bureau (2017).
Dates
......
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