Housing and the Great Recession: A VAR Accounting Exercise
Bibliographic record
Abstract
We use a vector autoregression (VAR) for the components of gross domestic product (GDP) to conduct some sectoral and temporal accounting for the current recession. It is obvious that housing played an important role in the current recession, but residential investment declined for two years before GDP declined. According to the VAR, the level of GDP in the second quarter of 2009 -- the trough of the decline in GDP -- was close to but above the level implied by the estimated sequence of VAR innovations to residential investment over the period 2006:Q1-2009:Q2. Until late 2007 other offsetting shocks kept real GDP growing roughly at trend, but after that the other shocks disappeared or reversed sign. Taking a similar approach with employment, we first observe that, as with output, employment in the housing industry began to fall well before aggregate employment. However, unlike output, the eventual decline in aggregate employment dwarfed the decline in housing-industry employment. The shock to residential construction employment can nonetheless explain a small portion of the current employment shortfall relative to trend.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".