Forecasting GDP growth : a comprehensive comparison of employing machine learning algorithms and time series regression models
Bibliographic record
Abstract
In this paper, we do a comprehensive comparison of forecasting Gross Domestic\nProduct (GDP) growth using Machine Learning algorithms and traditional time\nseries regression models on the following economies: Australia, Canada, Euro Area,\nGermany, Spain, France, Japan, Sweden, Great Britain and USA. The ML algorithms\nwe employ are Bayesian Additive Trees Regression Trees (BART), Elastic-Net\nRegularized Generalized Linear Models (GLMNET), Stochastic Gradient Boosting\n(GBM) and eXtreme Gradient Boosting (XGBoost), while Autoregressive (AR) models,\nAutoregressive Integrated Moving Average (ARIMA) models and Vector Autoregressive\n(VAR) models represents the traditional time series regression methods. The results\nassert that the multivariate VAR models are superior, indicating the chosen variables’\nand the models’ suitability of forecasting GDP growth. Furthermore, we also do\nan assessment of the top three variables that drives the best performing Machine\nLearning algorithm of XGBoost to investigate whether it suggests the same variables\nin forecasting GDP growth as macroeconomic theory. In general we do see some\nevidence, but in many cases the algorithm emphasizes other variables than what\nmacroeconomic theory suggests.\nKeywords – Time Series, Machine Learning, Econometric, GDP, Forecast
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".