Determinants of housing price volatility in Canada: a dynamic analysis
Bibliographic record
Abstract
This article tries to identify the determinants of housing price volatility and to examine the dynamic effects of these determinants on volatility using quarterly data for Canada. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) and the Vector Autoregressive (VAR) models have been employed to analyse possible time variation of the housing price volatility and the interactions between the volatility and the key macroeconomic variables. We find the evidence of time varying housing price volatility for Canada. Our VAR, Granger causality and variance decomposition (VDC) analyses demonstrate that housing price volatility is affected significantly by gross domestic product (GDP) growth rate, housing price appreciation rate and inflation. On the other hand, volatility affects GDP growth rate, housing price appreciation and volatility itself. The impulse response analysis reveals the asymmetric of the positive and negative shocks. The findings of this article have important implications, particularly for those seeking to develop derivatives for housing market prices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".