1. Bayesian Estimation

• 1.1. Restrictions on parameters during estimation
• 1.2. Prior distributions and implementation
  • 1.2.1. Bayesian priors
  • 1.2.2. Endogenous Priors
  • 1.2.3. User-defined Priors
  • 1.2.4. Visualizing priors and posteriors
  • 1.2.5. List of prior distributions
• 1.3. Posterior maximization
  • 1.3.1. Choosing an optimizer
  • 1.3.2. Bundled optimizers
  • 1.3.3. Running several optimizers in turn
  • 1.3.4. User-defined optimizers
• 1.4. Posterior sampling
  • 1.4.1. The sampling function
  • 1.4.2. Algorithm : Random-walk Metropolis Hastings
  • 1.4.3. Algorithm : Independent Metropolis Hastings
  • 1.4.4. Algorithm : Adaptive parallel tempering
  • 1.4.5. Algorithm : Slice Sampler
  • 1.4.6. Algorithm : usrsmplr Sampler
  • 1.4.7. User-defined Algorithms: Another approach
• 1.5. Processing Posterior draws : The mcmc class
  • 1.5.1. The properties
  • 1.5.2. The methods
  • 1.5.3. Visualizing posteriors (and priors)
• 1.6. Marginal Data Density computation : the mdd class
  • 1.6.1. The properties
  • 1.6.2. The methods
• 1.7. Indirect Inference
  • 1.7.1. The principle of indirect inference
  • 1.7.2. Indirect inference in RISE
  • 1.7.3. Generalized method of moments
  • 1.7.4. Simulated method of moments
  • 1.7.5. Impulse-response matching