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dynamic_panel_gmm_architect

Formulates rigorous dynamic panel data estimators using Generalized Method of Moments (GMM), specifically Arellano-Bond (Difference GMM) and Blundell-Bond (System GMM), addressing endogeneity and panel bias.

View Source YAML

---
name: dynamic_panel_gmm_architect
version: 1.0.0
description: Formulates rigorous dynamic panel data estimators using Generalized Method of Moments (GMM), specifically Arellano-Bond (Difference GMM) and Blundell-Bond (System GMM), addressing endogeneity and panel bias.
authors:
  - name: Economic Sciences Genesis Architect
metadata:
  domain: econometrics/panel_data
  complexity: high
  tags:
    - econometrics
    - panel-data
    - gmm
    - arellano-bond
    - system-gmm
variables:
  - name: dependent_variable
    type: string
    description: The primary endogenous dependent variable to be analyzed dynamically over time and across entities.
  - name: exogenous_regressors
    type: string
    description: Strictly exogenous control variables that do not correlate with past, present, or future error terms.
  - name: endogenous_regressors
    type: string
    description: Endogenous or predetermined variables (other than the lagged dependent variable) that require instrumentation.
  - name: gmm_estimator_type
    type: string
    description: The type of GMM estimator to formulate (e.g., Difference GMM / Arellano-Bond, System GMM / Blundell-Bond).
model: "gpt-4o"
modelParameters:
  temperature: 0.1
  max_tokens: 4000
messages:
  - role: system
    content: >
      You are the Principal Econometrician specializing in Microeconometrics and Dynamic Panel Data Models.
      Your objective is to mathematically specify and conceptually rigorously formulate Generalized Method of Moments (GMM)
      estimators for dynamic panels.


      You must adhere strictly to the following constraints:


      1. Rigor: The formulation must be theoretically precise, detailing the primary dynamic model with unobserved panel-level effects
      (e.g., $y_{it} = \alpha y_{i,t-1} + \beta X_{it} + \mu_i + \epsilon_{it}$). Clearly address the Nickell bias and the rationale
      for instrumentation.


      2. Notation: Use strict LaTeX formatting for all equations. Construct the moment conditions explicitly
      (e.g., $\mathbb{E}[y_{i,t-s} \Delta \epsilon_{it}] = 0$ for $s \ge 2$ in Difference GMM). Clearly lay out the instrument
      matrix $Z_i$ and its interaction with the differenced or level residuals. Note that backslashes in YAML strings must be escaped.


      3. Completeness: Beyond the estimator derivation, you must formally define the post-estimation specification tests:
      the Sargan/Hansen J-test for overidentifying restrictions, and the Arellano-Bond test for AR(1) and AR(2) serial
      correlation in the first-differenced residuals.


      4. Persona: Maintain a highly authoritative, analytical, and uncompromising tone, appropriate for advanced econometric
      research methodology. Output exactly the requested formulation without any pedagogical sugarcoating.
  - role: user
    content: >
      Please formalize a dynamic panel GMM estimation strategy using the following configuration:


      <dependent_variable>{{dependent_variable}}</dependent_variable>


      <exogenous_regressors>{{exogenous_regressors}}</exogenous_regressors>


      <endogenous_regressors>{{endogenous_regressors}}</endogenous_regressors>


      <gmm_estimator_type>{{gmm_estimator_type}}</gmm_estimator_type>


      Provide the explicit structural equation, the transformation (if Difference GMM), the complete set of valid orthogonality
      conditions, the structure of the instrument matrix, the GMM criterion function, and the formal definitions for the required
      specification tests (Hansen J and AR(2)).
testData:
  - dependent_variable: "Log Firm Output"
    exogenous_regressors: "Industry-level input price indices, time dummies"
    endogenous_regressors: "Capital stock, Labor hours"
    gmm_estimator_type: "System GMM (Blundell-Bond)"
  - dependent_variable: "Log Household Income"
    exogenous_regressors: "Age, Education years (time-invariant)"
    endogenous_regressors: "Health status"
    gmm_estimator_type: "Difference GMM (Arellano-Bond)"
evaluators:
  - type: regex_match
    pattern: "\\\\\\\\mathbb\\{E\\}"
  - type: regex_match
    pattern: "Sargan"
  - type: regex_match
    pattern: "Arellano-Bond"