{
  "context": "This criteria evaluates how effectively an engineer uses the spreg package to implement regime error separation, specifically using the OLS_Regimes class with the regime_err_sep parameter to run completely independent regressions for each spatial regime rather than pooled or partially-constrained models.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "OLS_Regimes class usage",
      "description": "Uses the spreg.OLS_Regimes class (not just spreg.OLS) to perform regime-based regression modeling, demonstrating understanding that this is the appropriate class for regime heterogeneity analysis.",
      "max_score": 25
    },
    {
      "name": "regime_err_sep parameter",
      "description": "Correctly sets the regime_err_sep parameter to True when calling OLS_Regimes, which is the key parameter that triggers separate regressions per regime rather than a single pooled regression with regime-varying coefficients.",
      "max_score": 30
    },
    {
      "name": "Regimes array specification",
      "description": "Properly passes the regime identifiers to the regimes parameter of OLS_Regimes, ensuring that observations are correctly assigned to their respective spatial regimes for separate estimation.",
      "max_score": 15
    },
    {
      "name": "Spatial weights integration",
      "description": "Correctly passes the spatial weights matrix (W) to the w parameter of OLS_Regimes, integrating spatial structure into the regime-separated analysis and enabling spatial diagnostics if needed.",
      "max_score": 10
    },
    {
      "name": "Model results extraction",
      "description": "Successfully accesses regime-specific results from the fitted model object, such as extracting individual regime coefficients (model.betas for each regime) and R-squared values (model.r2 or regime-specific fit measures), demonstrating understanding of the multi-regression output structure.",
      "max_score": 15
    },
    {
      "name": "Comparison baseline",
      "description": "Implements a comparison by also estimating a standard OLS model (using spreg.OLS without regimes) to serve as a pooled baseline, highlighting the value of regime separation through model comparison.",
      "max_score": 5
    }
  ]
}