India Macro-Market Indicators Dataset
A documented dataset design linking official macro releases to dated market observations without look-ahead bias.
Research context: Apply this methodology with Stock screener methodology, Industry statistics sourcebook, Listed companies dataset guide and Point-in-time backtesting.
What this resource does
Macro-market research requires two times: the economic reference period and the publication time when the market could first know the value. Joining only on month or quarter can introduce future information.
The dataset design keeps raw releases, revisions, calendar metadata, market timestamps, transformation versions, and missing states. It supports analysis but does not imply that macro indicators determine short-term prices.
Methodology
- Register official series for prices, output, rates, liquidity, currency, and external conditions.
- Store reference period, first release time, revision time, unit, and seasonal treatment.
- Join market observations only after the applicable release timestamp.
- Version transformations such as growth rates, surprises, lags, and rolling statistics.
How to interpret it
A market can anticipate a release, react to the difference from expectations, or focus on another event. The dataset separates association from causal claims.
Revised macro history is useful for current economic understanding; first-release vintages are necessary for honest historical model evaluation.
Limitations and failure modes
- Release calendars and source endpoints can change.
- Expectations data may have separate licensing and coverage.
- Many macro relationships are regime-dependent.
- Correlation does not establish a tradable or causal signal.
Research workflow
- Ingest official releases.
- Validate timestamps and revisions.
- Create point-in-time joins.
- Publish schema and coverage reports.
Questions and answers
What is look-ahead bias?
It occurs when an analysis uses information that was not yet available at the historical decision time, making results unrealistically strong.
Why store first release and revised values?
First releases support point-in-time evaluation; revised values support the best current description of the historical economy.