Regime Switches in GDP Growth and Volatility: Some International Evidence and Implications for Modelling Business Cycles
Bibliographic record
Abstract
This paper has three main objectives. First, we re-examine some recent findings that suggest a structural decline in the variance of GDP growth in the United States. We estimate a univariate model in which both the mean growth rate of GDP and its variance are influenced by latent state variables that follow independent Markov chain processes. We are particularly interested in evidence of increased stability in the U.S. economy, either because of reduced volatility or a narrower gap between growth rates in expansions and recessions. Second, we investigate whether a similar phenomenon has occured in other countries. Finally, we explore the extent to which this more general model is better able to describe the shape of actual business cycles. We find evidence of a reduction in GDP volatility in U.S. data, beginning in late 1984. However, it is less clear that this change represents a structural break. The recent U.S. recession has reduced the probability of being in the low-variance state. Using data from Australia, Canada, Germany, Japan and the United Kingdom, we find evidence of a similar reduction in volatility of GDP growth. The shift for Japan apparently happened in about 1974, and the past decade's poor economic performance seems to have brought a return to the high-variance state. Apart from Germany, the variance reductions in the other countries all occurred within a ten year period between the early 1980's and the early 1990's. Finally, when we test for non-linear effects using Bayes factors, we find that allowing for a switching variance is much more important than a switching mean. Although the hypothesis of homoscedasticity is overwhelmingly rejected, there is little evidence that this model is better able to capture the shape of actual business cycles.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".