Neural networks for macroeconomic forecasting: a complementary approach to linear regression models. Working Paper 2000-07
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mattina of Finance Canada and Alain Paquet of UQAM for their helpful comments. The views expressed in this paper are solely those of the author. No responsibility for them should be attributed to the Department of Finance. Working papers are intended to make analytical work undertaken at the Department of Finance available to a wider readership. They have received only limited evaluation and do not reflect the views of the Department of Finance. Comments on working papers are In recent years, neural networks have received an increasing amount of attention among macroeconomic forecasters because of their potential to detect and reproduce linear and nonlinear relationships among a set of variables. This paper provides a highly accessible introduction to neural networks and establishes several parallels with standard econometric techniques. To facilitate the presentation, an empirical example is developed to forecast Canada's real GDP growth. For both the in-sample and out-ofsample periods, the forecasting accuracy of the neural network is found to be superior to a well-established linear regression model developed in the Department, with the error
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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