OECD Journal: Journal of Business Cycle Measurement and Analysis

Frequency :
3 fois par an
ISSN :
1995-2899 (en ligne)
ISSN :
1995-2880 (imprimé)
DOI :
10.1787/19952899
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OECD Journal: Journal of Business Cycle Measurement and Analysis is jointly published by the OECD and the Centre for International Research on Economic Tendency Surveys (CIRET) to promote the exchange of knowledge and information on theoretical and operational aspects of economic cycle research, involving both measurement and analysis (see www.ciret.org/jbcma). Published as a part of the OECD Journal package.

 
 
 

Volume 2012, Numéro 2 You do not have access to this content

Date de publication :
28 mai 2013
DOI :
10.1787/jbcma-v2012-2-en

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  28 mai 2013 Cliquez pour accéder: 
    http://oecd.metastore.ingenta.com/content/3312021ec005.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/economics/constructing-coincident-and-leading-indices-of-economic-activity-for-the-brazilian-economy_jbcma-2012-5k4841782xnn
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Constructing coincident and leading indices of economic activity for the Brazilian economy
João Victor Issler, Hilton Hostalacio Notini, Claudia Fontoura Rodrigues

This paper has three original contributions. The first is the reconstruction effort of the series of employment and income to allow the creation of a new coincident index for the Brazilian economic activity. The second is the construction of a coincident index of the economic activity for Brazil, and from it, (re) establish a chronology of recessions in the recent past of the Brazilian economy. The coincident index follows the methodology proposed by The Conference Board (TCB) and it covers the period 1980:1 to 2007:11. The third is the construction and evaluation of many leading indicators of economic activity for Brazil which fills an important gap in the Brazilian Business-Cycle literature.

Keywords: Coincident and Leading Indicators, Business Cycles, Common Features, Latent Factor Analysis

JEL codes: C32, E32

  28 mai 2013 Cliquez pour accéder: 
    http://oecd.metastore.ingenta.com/content/3312021ec004.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/economics/day-of-the-week-effect-in-consumer-confidence-index_jbcma-2012-5k49j33nqdg5
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Day-of-the-week effect in Consumer Confidence Index
Sadullah Çelik, Hüseyin Kaya
The aim of this study is to examine the validity of the day-of-the-week effect on both mean and volatility for changes in Consumer Confidence Index in Turkey. To the best of our knowledge, there is no previous study on this topic for an emerging market. Employing the E-GARCH method, we are able to validate day-of-the-week effect both in mean and volatility of the daily changes in the Consumer Confidence Index. In our findings, the mean equation exhibits only a Friday effect and the lowest volatility is also observed for Friday. Additionally, we use nonparametric stochastic dominance (SD) approach by employing several SD tests and verify the existence of Friday effects.
  28 mai 2013 Cliquez pour accéder: 
    http://oecd.metastore.ingenta.com/content/3312021ec003.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/economics/heuristic-model-selection-for-leading-indicators-in-russia-and-germany_jbcma-2012-5k49pkpbf76j
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Heuristic model selection for leading indicators in Russia and Germany
Ivan Savin, Peter Winker

Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling "holes" in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany.

JEL classification: C52, C61, E37
Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms

  28 mai 2013 Cliquez pour accéder: 
    http://oecd.metastore.ingenta.com/content/3312021ec002.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/economics/measuring-capacity-utilisation-in-the-italian-manufacturing-sector_jbcma-2012-5k8znwp2nts8
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Measuring capacity utilisation in the italian manufacturing sector
Marco Malgarini, Antonio Paradiso

The aim of this paper is to provide an interpretation of the measure of capacity utilisation provided by the European Union harmonised survey on the Italian manufacturing sector. In doing so, we evaluate its ability to correctly track cyclical turning points and its contribution in explaining consumer price index (CPI) inflation. The survey based measure results are a good co-incident indicator of business cycle, however it is generally outperformed by time series models in explaining inflation. We conclude that the standard "output gap" interpretation of the survey results is broadly confirmed by the data, however we cannot rule out at this stage that survey respondents may also consider the alternative "variable capacity utilisation" concept in answering the survey question.

Keywords: Capacity utilisation, co-integration, unobserved component models, VAR.
JEL Classification: E32, C22, E37

  28 mai 2013 Cliquez pour accéder: 
    http://oecd.metastore.ingenta.com/content/3312021ec001.pdf
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  • http://www.keepeek.com/Digital-Asset-Management/oecd/economics/nowcasting-irish-gdp_jbcma-2012-5k92n2pwccwb
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Nowcasting Irish GDP
Antonello D'Agostino, Kieran McQuinn, Derry O’Brien

This paper presents a dynamic factor model that produces nowcasts and backcasts of Irish quarterly GDP using timely data from a panel dataset of 35 indicators. We apply a recently developed methodology, whereby numerous potentially useful indicator series for Irish GDP can be availed of in a parsimonious manner and the unsynchronised nature of the release calendar for a wide range of higher frequency indicators can be handled. The nowcasts in this paper are generated by using dynamic factor analysis to extract common factors from the panel dataset. Bridge equations are then used to relate these factors to quarterly GDP estimates. We conduct an out-of-sample forecasting simulation exercise, where the results of the nowcasting exercise are compared with those of a standard benchmark model.

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