The usefulness of Google Trends data in macroeconomic forecasting: Evidence from Finland
Bibliographic record
Abstract
This thesis looks into Google Trends data, its special features and uses in macroeconomic forecasting. Evidence is gathered from the literature to assess whether including Google Trends data in forecasting models improves their predictive accuracy. Google Trends data has many particularities, but nevertheless all papers included in the analysis find evidence in favor of Google Trends data being at least somewhat useful. Due to the differing modeling choices and sometimes conflicting results, it is not possible to draw exact conclusions on the conditions under which Google Trends data is useful. The hypothesis of the usefulness of Google Trends data is also tested empirically in Finnish context by estimating a dynamic factor model and by comparing the out-of-sample forecasting performance of models with and without Google Trends data. Inclusion of Google Trends data is found to not improve the forecasting performance significantly. Especially the poor nowcasting performance of the Google Trends data augmented models is surprising considering the previous literature. A robustness check suggests that the nowcasting ability of the tested models depend on the phase of the economic cycle, the models perform better during economic expansion and recovery and considerably worse during recessions and economic slowdowns. The nowcasting ability of the model is also found to increase the further in the quarter the nowcast is produced. It is not conclusive which search term selection method is optimal
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".