Bibliographic record
Abstract
Purpose The recent international financial crisis and the subsequent Great Recession has underlined the need to gain a better understanding of the linkages between financial factors and the real economy. The purpose of this paper is to explore the contributions of financial shocks to macroeconomic fluctuations in Denmark, USA and Canada over the past century. Design/methodology/approach The paper compiles a new data set with break-adjusted quarterly time series of real gross domestic product (GDP) and six other key macroeconomic indicators for the three countries since 1921. It then explores the time-variation of macro-financial linkages by estimating structural vector-autoregressive models separately for three sample periods: 1921-1949, 1950-1979 and 1980-2014. Findings For all three countries, there seems to have been a non-trivial contribution from financial shocks to volatility in output and unemployment in all sample periods, even in the period 1950-1979, which was characterised by tight regulation of the financial sector and widespread financial stability. These findings underscore the general importance of financial factors to macroeconomic fluctuations. Originality/value The paper is the first to offer regression-based estimates of quarterly real GDP for Denmark for 1921-1947. As a result, quarterly figures for real GDP are now available for Denmark for the entire period 1921-2014. Such long-span time series of quarterly real GDP do not exist for any other European countries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".