{
  "title": "Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods",
  "publication": "IMF Working Papers, April 1, 2009",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2016/12/31/limited-information-bayesian-model-averaging-for-dynamic-panels-with-short-time-periods-22813",
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  "summary": "Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors.",
  "sections": [
    {
      "heading": "Summary and purpose",
      "content": "- Extends the Bayesian Model Averaging (BMA) framework to panel data models that include the lagged dependent variable and endogenous regressors.\n- Proposes a Limited Information Bayesian Model Averaging (LIBMA) methodology intended for dynamic panel data models with short time periods under model uncertainty.\n- Tests LIBMA using simulated data to evaluate performance in model selection and parameter estimation."
    },
    {
      "heading": "Methodology",
      "content": "- LIBMA: a limited-information extension of BMA tailored to dynamic panels with endogenous variables and lagged dependent variables as regressors.\n- Approach targets inference when time dimension is short and model uncertainty is present."
    },
    {
      "heading": "Simulation results and findings",
      "content": "- Asymptotic performance: simulation results suggest LIBMA performs well both in Bayesian model selection and in averaging.\n- Data-generating process recovery: LIBMA \"recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values.\"\n- Implication: methodology is well suited for inference in dynamic panel data models with short time periods and endogenous regressors under model uncertainty."
    },
    {
      "heading": "Subjects and keywords",
      "content": "- Subject: Bayesian models, Data processing, Estimation techniques\n- Keywords: mover accent, WP\n\nAlin T Mirestean, Charalambos G Tsangarides, and Huigang Chen. \"Limited Information Bayesian Model Averaging for Dynamic Panels with Short Time Periods\", IMF Working Papers 2009, 074 (2009), accessed 9/17/2026\n\n---\n\n Content in this bundle\n\n- wp0974\n  - wp0974 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - wp0974 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2016/12/31/limited-information-bayesian-model-averaging-for-dynamic-panels-with-short-time-periods-22813"
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    "Authors: Alin T Mirestean, Charalambos G Tsangarides, Huigang Chen",
    "Published: April 1, 2009",
    "Series: IMF Working Papers",
    "Extends the Bayesian Model Averaging (BMA) framework to panel data models that include the lagged dependent variable and endogenous regressors.",
    "Proposes a Limited Information Bayesian Model Averaging (LIBMA) methodology intended for dynamic panel data models with short time periods under model uncertainty.",
    "Tests LIBMA using simulated data to evaluate performance in model selection and parameter estimation.",
    "LIBMA: a limited-information extension of BMA tailored to dynamic panels with endogenous variables and lagged dependent variables as regressors.",
    "Approach targets inference when time dimension is short and model uncertainty is present.",
    "Asymptotic performance: simulation results suggest LIBMA performs well both in Bayesian model selection and in averaging.",
    "Data-generating process recovery: LIBMA \"recovers the data generating process very well, with high posterior inclusion probabilities for all the relevant regressors, and parameter estimates very close to the true values.\"",
    "Implication: methodology is well suited for inference in dynamic panel data models with short time periods and endogenous regressors under model uncertainty.",
    "Subject: Bayesian models, Data processing, Estimation techniques",
    "Keywords: mover accent, WP",
    "**_wp0974**"
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