In macroeconomic analysis, data types specify the structural forms in which system-level economic activity is recorded prior to any analysis, modeling, or policy interpretation. These forms determine which aggregate patterns are observable, which dynamics can be resolved, and what information is irreversibly lost at capture. Data types therefore fix the evidentiary limits of macroeconomic inquiry.
1. Time Series
The dominant data type in Aggregation is the time series.
Examples include GDP, inflation, unemployment, output, productivity, monetary aggregates, fiscal balances, and other macroeconomic indicators recorded at regular intervals. Time-series structure enables analysis of trends, cycles, growth paths, and regime shifts, but constrains inference to the temporal resolution, continuity, and revision structure of reporting.
2. Spectra and Distributions
Aggregate data may appear as distributions over populations or economic units.
Examples include income and wealth distributions, firm-size distributions, sectoral output shares, or productivity distributions. These formats capture heterogeneity within the system while suppressing individual trajectories, interaction-level sequencing, and causal detail.
3. Images, Volumes, and Spatial Fields
Spatial data are secondary but sometimes relevant in Aggregation.
Examples include geographic maps of economic activity, regional heatmaps of employment or prices, or spatial fields derived from regional aggregates. These representations encode spatial variation but are typically constructed from tabular or time-series records rather than collected as primary macroeconomic evidence.
4. Tables, Counts, and Matrices
A core data type in Aggregation is structured tabular data.
Examples include national accounts tables, input–output matrices, sectoral balance sheets, flow-of-funds tables, and cross-country comparison tables. This format preserves accounting relationships and system structure while abstracting away from individual or transactional detail.
5. Curves and Derived Plots
Aggregate data are frequently summarized as curves or plots derived from system records.
Examples include Phillips curves, yield curves, growth trajectories, impulse-response plots, or aggregate demand and supply curves. These are derived representations, not raw evidence, and their validity depends entirely on the stability, resolution, and completeness of the underlying aggregate data.
6. Symbolic or Structural Data
Aggregation may include formal or encoded representations of macroeconomic structure.
Examples include accounting identities, sectoral classification systems, balance constraints, or encoded policy rules. These data types preserve structural relationships but function as evidence only insofar as they are instantiated in recorded aggregate data.
Structural Limits (Aggregation)
Across all data types, Aggregation-based evidence is fundamentally:
- System-level rather than individual
- Population- and institution-aggregated
- Time-indexed and continuity-dependent
- Resolution-limited by reporting and accounting conventions
Information not captured at this stage—such as micro-level decisions or interaction sequencing—cannot be recovered downstream.