Threshold autoregressive (TAR) &Momentum Threshold autoregressive (MTAR) models Specification
Bibliographic record
Abstract
This paper proposes a testing integration and threshold integration procedure of interest rate and inflation rate. It locates whether there have been a cointegrating relationship between them and recognizes the procedures of addressing structural break. The most important issue is the testing of the hypothesis that whether effect of inflation on interest rates depends on the movement of inflation declining or increasing. In this study we analyze the inflation and interest rate of Canada for their long term relationships by applying co integration technique of EG-Model. Further we test it for the structural break and threshold autoregressive (TAR) and Momentum Autoregressive (MTAR) integration and stationarity. We generate real interest rate and test it for the same. We come to an end that TAR best capture the adjustment process. We discover that our selected series are integrated at level one. There is cointegration relationship between interest rate and inflation with the cointegrating vector (1,-1).We find no asymmetry in our series and therefore conclude that there is same effect of inflation increase or decrease on interest rate. Keywords: Threshold autoregressive, Momentum autoregressive, Co integration, Stationarity
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".