Econometric estimation of portfolio balance and monetary models of exchange rate determination: The case of Canada
Bibliographic record
Abstract
The primary purpose of this dissertation is to present, solve, and estimate a portfolio balance model and its extensions for the Canadian/U.S. exchange rate, and evaluate the out-of-sample forecasting performance of each of these models by comparing it to that of the random walk model. Current literature reveals the structural models of exchange rate determination to be deficient in explaining exchange rate movements. In particular, we find the empirical validity of monetary models to be worth examining, given the abundance of literature pointing to their demise. Our purpose is to show that the pessimism with reference to these models is unfounded, at least for the Canadian/U.S. exchange rate. We apply the most recently developed non-stationarity and cointegration techniques to examine the time-series properties of the variables. Each of these models is then appropriately estimated given these underlying time-series properties. Another objective of this dissertation is to test the cross-equation restrictions imposed by the rational expectations solution, This involves a joint test of the structural model and the rational expectations hypothesis,; Finally, the out-of-sample prediction properties of these structural models are analyzed. We will provide evidence that at least one of our structural models is capable of out-performing the simple random walk model. We estimate our models for Canada using quarterly data for the period 1971 to 1992, Based on the likelihood ratio test, we cannot reject the joint hypothesis of any of our structural models and the rational expectations assumption. We also find evidence that the structural exchange rate models are quite capable of out-performing the simple random walk model, at least for the Canadian/U.S. exchange rate. We find our results to be interesting because previous researchers in this field have provided evidence against structural exchange rate models. By modifying a few assumptions in the traditional structural models, we succeed in showing that the structural models are still very much alive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".