Real-Time Transaction Data for Nowcasting and Short-Term Economic Forecasting
Bibliographic record
Abstract
Abstract Transaction data from consumer purchases is used for monitoring, nowcasting, or short-term forecasting of important macroeconomic aggregates such as personal consumption expenditure and national income. Data on individual purchase transactions, recorded electronically at point of sale or online, offer the potential for accurate and rapid estimation of retail sales expenditure, itself an important component of personal consumption expenditure and therefore of national income. Such data may therefore allow policymakers to base actions on more up-to-date estimates of the state of the economy. However, while transaction data may be obtained from a number of sources, such as national payments systems, individual banks, or financial technology companies, data from each of these sources contain limitations. Data sets will differ in the forms of information contained in a record, the degree to which the samples are representative of the relevant population of consumers, and the different types of payments that are observed and captured in the record. As well, the commercial nature of the data may imply constraints on the researcher’s ability to make data sets available for replication. Regardless of the source, the data will generally require filtering and aggregation in order to provide a clear signal of changes in economic activity. The resulting series may be incorporated into any of a variety of model types, along with other data, for nowcasting and short-term forecasting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".