Essays in applied structural macro-econometric modelling
Bibliographic record
Abstract
This thesis is a compilation of three diverse but self-contained chapters on the impact of monetary policy shocks on six Small-Open-Economies (SOEs) and the US and the impact of commodity demand and supply shocks on the Australian economy. In the first chapter, we estimate Small-Open-Economy-Structural Vector Autoregression (SOE-SVAR) models for Australia, Canada, New Zealand, Norway, Sweden and the United Kingdom to measure the effects of SOE and US monetary policy shocks on bilateral SOE/US exchange rates. We find that a contractionary SOE (US) monetary shock triggers an immediate appreciation (depreciation) of the exchange rate followed by a reversion, in line with Dornbusch’s overshooting and uncovered interest rate parity. SOE monetary impulses account for a greater portion of the short-run volatility of the exchange rate than US monetary shocks. In the second chapter, I estimate SVAR models to measure the effects of commodity supply and commodity demand shocks on Australia’s output and trade balance. I find that commodity supply and demand shocks emerge as a relatively minor and negligible sources of business cycle fluctuations in output and trade balance. I further find that output expands (contracts) in response to commodity demand (commodity supply) shocks. Interestingly, for both commodity supply and demand shocks the trade balance worsens substantially. In the third chapter, I review the literature on proxy-SVAR models and document the evolution of various types of proxies for US monetary policy shocks. The chapter also contains an application of proxy-SVAR models using high-frequency monetary policy instruments for the US. I compare the two most recent US monetary instruments and find that information-robust monetary instrument produces different results compared to the instrument that does not take into account the information content of monetary policy announcements.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".