Chapter 12: Posterior Simulation Via Markov Chain Monte Carlo

Exercises, Programs and Files:

• 12.3: Metropolis-Hastings Algorithm II
  • Matlab Code

• 12.4: Slice Sampling I
  • slice_sampling_3x2.m

• 12.5: Slice Sampling II
  • slice_mixture3.m

• 12.6: Gibbs sampling: A Comprehensive Introductory Example
  • fit_lightbulb.m
    • Supporting File:
      • lightbulbdata

• 12.7: Gibbs Sampling in a Count Data Model with an Unknown Changepoint
  • count_changepoint.m
    • Supporting File:
      • coaldata.txt (Coal Disaster Data)

• 12.8: Gibbs Sampling from the Bivariate Normal
  • Matlab Program

• 12.10: Gibbs Sampling in a Regression Model with an Unknown Changepoint
  • reg_changepoint.m

• 12.13: Metropolis-Within-Gibbs in a Model of Parametric Heteroscedasticity
  • Matlab Code
    • Supporting File:
      • HPRICE.txt (Data)
      • auxiliary file (needed to execute main program)

• 12.14: Heteroscedasticity of an Unknown Form: Student-t Errors
  • Matlab Code
    • Supporting File:
      • HPRICE.txt (Data)

• 12.15: Autocorrelated Errors
  • Matlab Code
    • Supporting File:
      • YANKEES.txt (Data)

• 12.16: Moving Average Errors
  • linreg_ma1.m
    • Supporting Files:
    • YANKEES.txt (Data)
    • like_MA1.m

• 12.17: Gibbs Sampling in a Regresion Model with Inequality Constraints: Geweke (1996b)
  • manufact_gibbs.m (Fits unconstrained model)
  • manufact_diminish_000.m (Fits constrained model)
    • Supporting Files:
      • raw_cigdata.txt (Data)
      • clean_prodfun.m (Cleans Data - execute first)

• 12.18: Gibbs Sampling in the SUR Model
  • gibbs_sur_000.m

• 12.19: Error Diagnostics with Gibbs Sampling
  • diagnostics.m

• 12.21: Diagnosing MCMC Convergence I
  • gibbs_convergence.m

• 12.23: Checking for Errors in a Posterior Simulator: Geweke (2004)
  • sim_check.m
    • Supporting File:
      • num_variance.m