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Record W288265715

Identifying Canadian Regional Business Cycles Using the Plucking Model

2004· article· en· W288265715 on OpenAlexvenueaboutno aff
Gabriel Rodrı́guez

Bibliographic record

VenueCanadian Journal of Regional Science · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness cycleEconomicsRecessionEconometricsUnemploymentAsymmetryKeynesian economicsMacroeconomicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

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. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.236
GPT teacher head0.268
Teacher spread0.032 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes2
Has abstractyes

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