The Sectoral Impact of Monetary Policy in Australia: A Structural VAR Approach
Bibliographic record
Abstract
In recent years, the global resources boom has had a major impact on the Australian economy. In the mining rich state of Western Australia, rapid commodity price growth has contributed to strong economic conditions. However, state economies that rely heavily on manufacturing industries have fared less well, forced to cope with higher input costs as well as the effects of a stronger exchange rate. The resulting 'two-speed economy' presents a challenge for monetary policy, which must manage the diverging performances of different sectors and regions. In light of these issues, this thesis develops a small, open economy structural vector autoregression (SVAR) model of Australia in order to examine the impact of monetary policy on sectoral output. The results suggest that monetary policy shocks have uneven impacts across different sectors. The construction and manufacturing sectors show the most sizeable and rapid responses, while the mining sector is not as interest rate sensitive as the existing literature would suggest. This thesis also adds to our understanding of the transmission mechanism of monetary policy in a small, open economy. In particular, while the results indicate that global economic conditions account for a large proportion of the variation in mining sector output, there is evidence that the exchange rate channel of monetary policy does not play a dominant role in influencing output in this sector. One implication of these findings is that the Reserve Bank of Australia will find it difficult to stabilise output across regional economies in the face of a resources boom. The model also indicates that changes to monetary policy have long, non-trivial real impacts, and there is some suggestion that the credit channel of monetary policy has an important influence in propagating monetary policy shocks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".