The Relationship between Different-term Interest Rate Spreadsand Economic activity: Evidence from the United States
Bibliographic record
Abstract
[[abstract]]Current literature about the relationship between interest rate spreads and economic activity has focused on the interest rate spread for long-term bonds such as 10-year Treasury bond. The importance of the interest rate spread for medium-term and short-term Treasury bonds as upcoming key predictors has been neglected. This study, therefore, aims to explore whether the medium-long-term (7-year) spread, the medium-short-term (5-year) spread, and the short-term spread (3-year) are as informative as the long-term (10-year) spread in predicting recessions. The variables used in this study include real GDP, consumer price index (CPI), money supply (M2), and the aforementioned four spreads. The data spanning over the period 1961.Q1-2011.Q3 are collected at the quarterly interval from the Taiwan Economic Journal. The unit-root test is first performed on each of the variables to detect stationarity. Four regression models are then specified to examine the relationship between real GDP growth and the four spreads and the significance of this relationship overtime. Evidence indicates that the spread lagged by at least the first five quarters exert significantly positive influences on real GDP growth for the four spreads if the lags are included as regressors individually. However, only the spreads lagged by one quarter remain significantly positive for the four spreads if all the lags are included as regressors in the model simultaneously. The medium-long-term spread, the medium-short-term spread, and the short-term spread are as informative as the long-term spread in predicting recessions. Evidence further shows that the positive relationship had turned to be weaker in Period II than In Period I for the four spreads. These findings emphasize the interest rate spread as an economic indicator for the Fed as well as investors to predict recession or future economic activity. Current literature about the relationship between interest rate spreads and economic activity has focused on the interest rate spread for long-term bonds such as 10-year Treasury bond. The importance of the interest rate spread for medium-term and short-term Treasury bonds as upcoming key predictors has been neglected. This study, therefore, aims to explore whether the medium-long-term (7-year) spread, the medium-short-term (5-year) spread, and the short-term spread (3-year) are as informative as the long-term (10-year) spread in predicting recessions. The variables used in this study include real GDP, consumer price index (CPI), money supply (M2), and the aforementioned four spreads. The data spanning over the period 1961.Q1-2011.Q3 are collected at the quarterly interval from the Taiwan Economic Journal. The unit-root test is first performed on each of the variables to detect stationarity. Four regression models are then specified to examine the relationship between real GDP growth and the four spreads and the significance of this relationship overtime. Evidence indicates that the spread lagged by at least the first five quarters exert significantly positive influences on real GDP growth for the four spreads if the lags are included as regressors individually. However, only the spreads lagged by one quarter remain significantly positive for the four spreads if all the lags are included as regressors in the model simultaneously. The medium-long-term spread, the medium-short-term spread, and the short-term spread are as informative as the long-term spread in predicting recessions. Evidence further shows that the positive relationship had turned to be weaker in Period II than In Period I for the four spreads. These findings emphasize the interest rate spread as an economic indicator for the Fed as well as investors to predict recession or future economic activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".