The simplification, solution and estimation of a small open DSGE model : Evidence from the UK and Canada
Bibliographic record
Abstract
This thesis makes three main contributions to the literature on Dynamic Stochastic General Equilibrium (DSGE) models. The first contribution is to bridge the gap between a theoretical small open DSGE model provided by Gali and Monacelli and an empirical model developed by Lubik and Schorfheide, as no previous studies have shown their relationship explicitly. Since all the models suffer from the misspecification problem to some extent, the second contribution is to apply two methodologies including DSGE-VAR approach and indirect inference to study the effect of the possibly misspecified equation of the change rate of terms of trade. The third contribution is to search for the model with the best data fitting in two stages of model comparisons. The thesis assumes that the parameters of the simplified DSGE model are constant at the first stage, and based on the constant parameter models with the best performance on data fitting, it assumes a subset of the parameters including exogenous shock variances and policy parameters follow two independent Markov-switching Markov chains at the second stage. The empirical results are quite different for the UK and Canada within the sample period covering 1992: Q4 – 2008: Q4. The UK data supports that the movement of the nominal exchange rate should not enter into the monetary policy reaction function. Also, the data supports that it is possible for the UK to experience the two kinds of structural changes, including the economic environment and the behaviours of policymakers simultaneously. Comparatively, Canadian data is in favour of the movement of the nominal exchange rate in the policy function. Moreover, the data supports that it is less likely for Canada to experience two kinds of structural changes simultaneously.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".