Model unable to output any successful draws in Gibbs sampling

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Alex
Alex 2024-8-4
回答： UDAYA PEDDIRAJU 2024-8-23
I am trying to estimate a structural VAR model using traditional sign and zero restrictions for the identification of shocks and Minnesota priors. When I adapt the (working) code to run on my data, I am unable to generate any successful draws in the Gibbs sampling phase. I have tried playing around with the settings of the Minnesota prior, as well as number of observations, number of variables, number of restrictions, running the model in First-Differences, and still can't get any successful draws. Does anyone have any ideas as to why this might be happening or anything I can try next?
Thanks in advance,
Alex

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回答（1 个）

UDAYA PEDDIRAJU
UDAYA PEDDIRAJU 2024-8-23
Hi Alex,
I understand you're facing issues with Gibbs sampling in your structural VAR model, here are some common reasons for this problem and potential remedies:
1. Prior Misspecification:
  • Overly tight priors: Relax the priors to allow for more variability in the posterior.
  • Incorrect prior form: Ensure the prior is conjugate to the likelihood for efficient sampling.
2. Data Issues:
  • Non-stationarity: Transform the data (e.g., first differencing) to achieve stationarity.
  • Outliers or missing values: Clean the data to remove outliers or handle missing values appropriately.
3. Model Misspecification:
  • Overly complex model: Simplify the model if it's too large or includes unnecessary variables.
  • Identification issues: Double-check your identification strategy and ensure it's appropriate for the model.
4. Gibbs Sampler Settings:
  • Insufficient iterations: Increase the number of iterations to allow the sampler to converge.
  • Burn-in period: Ensure a sufficient burn-in period to discard initial draws that may not represent the posterior distribution.
5. Other Considerations:
  • Initialization: Try different starting values for the Gibbs sampler.
  • Alternative samplers: Consider using other sampling methods like Metropolis-Hastings or Hamiltonian Monte Carlo.
Documentation reference: https://www.mathworks.com/help/econ/introduction-to-vector-autoregressive-var-models.html

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