Option A: ARX Time-Series
PrimaryAutoregressive model with exogenous intervention variables for forecasting and policy simulation
Option B: Quasi-Experimental
Secondary/ValidationDifference-in-differences or synthetic control for causal identification of intervention effects
Stage 1: ARX Forecasting
Extend OCEA AR(1) model with intervention indicators and policy-relevant exogenous variables
Stage 2: Causal Validation
Apply DiD/synthetic control to historical interventions to validate multiplier estimates
Stage 3: Decision Integration
Combine forecasts and causal estimates into multi-criteria ranking framework
Rationale for Hybrid Approach
- 1.Adjusted Baseline Compatibility: The OCEA inflation-adjusted, de-duplicated series (real 2024 USD) provides a clean dependent variable that avoids the systematic overestimation found in CAGR-based forecasts.
- 2.Policy Relevance: Government must evaluate interventions across heterogeneous objectives (commercial adoption, NASA missions, strategic competition) requiring both forecasting and causal attribution.
- 3.Data Limitations: Annual space economy observations (T≈15) favor parsimonious AR models; cross-sectional variation (by sector or country) enables quasi-experimental designs for specific interventions.
Dependent Variable
Y_tAdjusted real space economy value (GDPDEF or PPI-adjusted, double-counting removed)
Source: SIA adjusted series per OCEA methodology
Intervention Variables
X_tGovernment R&D appropriations, procurement contracts, regulatory changes, partnership announcements
Source: NASA budget data, FAA/FCC filings
Control Variables
Z_tGDP growth, interest rates, competing nation spending (China, ESA), technology readiness levels
Source: BEA, OECD, intelligence assessments