masashige.hamano@waseda.jp<\/a><\/p>\n\u63a8\u5968\u74b0\u5883\uff1aMatlab (core matlab, optimization toolbox, statistics toolbox)
\nMikTex, dropbox, oxedit (optional)<\/p>\n
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\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u8a73\u7d30<\/h2>\nLecture 1: Constant-parameter DSGE modeling and Introduction to RISE (10\/18)<\/h3>\n
A prototypical DSGE model
\nSolution strategies
\nThe RISE programming environment
\nImplementation of the model
\nCalibration
\nDeterministic solutions<\/p>\n
Lecture 2: Solving the steady state (10\/25)<\/h3>\n
Analytical solution: The steady state model
\nAnalytical solution: The steady state file
\nEndogenous parameters
\nNumerical solutions: Equation simplification
\nNumerical solutions: alternative solvers
\nNumerical solutions: constraints on the search space
\nNumerical solutions: block triangularization
\nThe balanced-growth path of nonstationary models<\/p>\n
Lecture 3: Stochastic solution via first-order perturbation (11\/1)<\/h3>\n
The dynamics
\nBasic usages of the solution
\nSimulations<\/p>\n
Lecture 4: Bayesian Estimation (11\/8)<\/h3>\n
Implementation of priors
\nMeasurement errors: importance and implementation
\nPosterior maximization
\nPosterior simulation
\nIllustrating uncertainty
\nEndogenous priors
\nIndirect inference<\/p>\n
Lecture 5: Optimal policy (11\/15)<\/h3>\n
Commitment
\nDiscretion
\nLoose commitment<\/p>\n
6. Lecture 6: Perturbation solutions of Regime Switching DSGE models with constant transition probabilities (11\/22)<\/h3>\n
A prototypical model and the RISE implementation
\nPerturbation approaches
\nMultiplicity of steady states and their implications
\nDynamic solution and mean-square stability
\nIndependent Markov processes
\nParameters controlled by several Markov processes<\/p>\n
7. Lecture7: Regime Switching DSGE models with time-varying transition probabilities (11\/29)<\/h3>\n
Endogenous probabilities in the prototypical model
\nHigher-order perturbation
\nDynamic solution and heuristic stability
\nComputation of moments
\nSimulation<\/p>\n
8. Lecture 8: Bayesian Estimation of Regime Switching DSGE models (12\/6)<\/h3>\n
Filtration and Likelihood computation in the presence of regime switching
\nState normalization and nonlinear constraints
\nPriors implementation
\nNonlinear constraints
\nPosterior maximization and posterior simulation<\/p>\n
9. Lecture 9: Further topics in Bayesian estimation (12\/13)<\/h3>\n
Computation of Marginal data density
\nConvergence diagnostics
\nModel comparison<\/p>\n
10. Lecture 10: Optimal Ramsey Policy under regime switching (12\/20)<\/h3>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
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