Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19
Bibliographic record
Abstract
"The spread of COVID-19 has caused large-scale loss of life and economic damage. This pandemic has had a swift effect on the macroeconomy, posing a new and different shock to the Canadian economy. Governments have responded in many ways, including through public health measures, fiscal stimulus and monetary policy. Policy-makers seek to understand the current state of the economy for their COVID-19 support to be effective. However, major economic indicators are released with a substantial delay. This problem is usually dealt with by using a linear model of past economic variables. But this is not the best approach presently, given the large and nonlinear effects of COVID-19 on the economy. In this paper, we develop a model to predict the current state of the economy—known as nowcasting—using retail payments system data and machine learning. The Canadian retail payments data aid in understanding the current state of the economy because they include many types of transactions and are available daily. These data features are ideal for macroeconomic nowcasting during a crisis. The flexibility of machine learning can help capture the large and nonlinear effects of the COVID-19 shock. We find that our model—compared with a benchmark model—has a significant increase in prediction accuracy, measured as a 15 to 45 percent reduction in forecast error."
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".