Forecasting GDP growth : a comprehensive comparison of employing machine learning algorithms and time series regression models
Why this work is in the frame
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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
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it