Does Economic Policy Uncertainty Forecast Real Housing Returns in a Panel of OECD Countries? A Bayesian Approach
Bibliographic record
Abstract
This paper investigates whether the news-based measure of economic policy uncertainty (EPU) could help in forecasting the real housing returns in ten (Canada, France, Germany, Italy, Japan, The Netherlands, South Korea, Spain, United Kingdom, and United States of America) Organization for Economic Co-operation and Development (OECD) countries. We analyze the quarterly out-of-sample period of 2008:Q2–2014:Q4, given an in-sample period of 2003:Q1–2008:1Q1, using time series and panel data-based Vector Autoregressive models, with the latter allowing for heterogeneity, and static and dynamic interdependence. It is found that regardless of the forecasting model considered, EPU is useful for forecasting real housing returns. Our results show that, panel data models, especially the Bayesian variants which allow for parameter shrinkage, consistently beat time series autoregressive models suggesting the importance of pooling information when trying to forecast real housing returns.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".