Time Series Tests of the Solow Growth Model
Bibliographic record
Abstract
We propose a new methodology in order to study the stability of output growth over 135 years for 19 OECD countries and 7 Asian countries. Previous research on economic growth can be categorized in three hypothesis: first, the level and the growth rate of long run income per capita is constant, second, policy changes have transitory effect on the growth rate of the economy and permanent effect on the income level and third, policy changes have permanent effect on the economy’s long run growth rate and income level. Our innovation consists in the fact that we formally test these hypotheses using time series tests techniques. We find evidence of constant income levels and growth rates for 2 countries which were the least affected by wars, US and Canada. For the second group of 7 OECD countries, strongly affected by the World War II, we find constant growth rate but a permanent change in the income level after a shock, showing evidence towards semi-endogenous growth models. Finally, there is a third group of 11 countries (all Asian countries and some European countries) where we find permanent changes in growth rate and income level after a shock, showing evidence towards endogenous growth models. “Consider the following simple exercise. An economist living in the year 1929 (who has miraculous access to historical per capita GDP data) fits a simple linear trend to the
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".