Identifying Canadian Regional Business Cycles Using the Plucking Model
Notice bibliographique
Résumé
Abstract Following the econometric specification suggested by Kim and Nelson (1999b), the model (Friedmann 1964, 1993) is tested using output data from the Canadian regions. The empirical results give strong support to the theoretical predictions that negative transitory shocks hit the economy putting down the real regional output. After that, the regional economies enter into a recovery phase and after this they are operating again near the trend ceiling level. The only exception is the Atlantic region where the linear symmetric model of Clark (1987) cannot be rejected. Using the estimated filtered probabilities, a chronology of the regional business cycles is presented. It shows that there are clear and particular episodes corresponding to the regional dynamics which are not necessarily present at the aggregate level. ********** It was Friedman (1964, 1993) who noted that the amplitude of a recession is strongly correlated with the following expansion, but the amplitude of an expansion is not correlated with the amplitude of the succeeding contraction. This striking asymmetry is the basic argument supporting the so named plucking model of business cycles. (1) Neftci (1984) presented empirical evidence of the kind of asymmetry advanced by Friedman (1964, 1993), when he found that unemployment rates are characterized by sudden jumps and slower declines. Further evidence was found by Delong and Summers (1986), Falk (1986), and Sichel (1993). As Kim and Nelson (1999b) say, while these kind of asymmetries are consistent with the model, they are also consistent with models where recessions are occasioned by infrequent permanent negative shocks as in the Markov-Switching models of Hamilton (1989) and Lain (1990). According to these authors, what distinguishes the model is the prediction that negative shocks are largely transitory, while positive shocks are largely permanent. (2) Another important characteristic of the model is the existence of an upper limit to the output, the so named ceiling output, which is set by the resources available in the economy. The fact that recessions can essentially result from occasional transitory shocks may suggest that a recession, once it begins, will dissipate in a fairly predictable period of time. However, the length of an expansion is not helpful in predicting the next recession. This is what in the literature of business cycles is called duration dependence, which was investigated by Diebold and Rudebusch (1990), Diebold, Rudebusch and Sichel (1993), and Durland and McCurdy (1994) in an univariate context; and Kim and Nelson (1998) in a multivariate context. All these references found empirical support for the existence of duration dependence only for recession times. Recently, Kim and Nelson (1999b) (3) suggested a formal econometric specification of the business cycle. Their specification allows us to decompose measures from economic activity into a trend component and deviations from the trend that show the types of asymmetries implied by the business cycle literature. In this sense, the approach offers more possibilities than standard linear models such as ARIMA models and the unobserved component model of Clark (1987), which cannot account for asymmetries. It may also perform better than other kind of models as the Markov-Switching (Hamilton 1989; Lam, 1990) where the asymmetric behavior is only accounted in the growth rate or stochastic trend component of real output. Mills and Wang (2002) applied to the output of the G-7 countries the approach of Kim and Nelson (1999b). Galvao (2002) noted an interesting performance of this approach, where this model is one of the three models capable of reproducing the length of the United States business cycles, in this respect, see the special issue about business cycles published by Empirical Economics in 2002. In this paper, I follow the same methodology of Kim and Nelson (1999b) applied to the logarithm of the real quarterly GDP of Canadian regions covering the period 1961:1 to 2000:1. …
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».