The Relationship between Different-term Interest Rate Spreadsand Economic activity: Evidence from the United States
Notice bibliographique
Résumé
[[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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».