Toward a more reliable picture of the economic activity: An application to Argentina

Camacho, Máximo; Bianco, Marcos Dal; Martínez-Martin, Jaime

doi:10.1016/j.econlet.2015.03.032

Cited by 15 publications

(16 citation statements)

References 3 publications

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“…There is evidence of shifts of the relationship between hard and soft indicators for the Euro zone as a whole as well as individual countries, sectors, and specific survey questions (see for instance (European Commission, 2016;Bruno, Crosilla, and Margani, 2016;de Largentaye and Roucher, 2015;Ferrara, Guégan, and Rakotomarolahy, 2010;Commission, 2011)). 25 See for instance Camacho and Perez-Quiros (2010); Camacho, Dal Bianco, and Martinez-Martin (2015); Camacho and Perez-Quiros (2011); Rusnák (2016); Barnett, Chauvet, and Leiva-Leon (2014); Luciani and Ricci (2014); Fenz and Spitzer (2006); Galli, Hepenstrick, and Scheufele (2017).…”

Section: Additional Variablesmentioning

confidence: 99%

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Business Cycle Dating and Forecasting with Real-Time Swiss GDP Data

Glocker

Wegmueller²

2019

SSRN Journal

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We develop a small-scale dynamic factor model for the Swiss economy based on an appropriately selected set of indicators. The resulting business cycle factor is in striking accordance with historical Swiss business cycle fluctuations. Our proposed model demonstrates a remarkable performance in short-term and medium-term forecasting. Using real-time GDP data since 2004, the model successfully anticipates the downturn of 2008-09 and responds in a timely manner to the recent sudden drop following the removal of the Swiss Franc lower bound. In a Markov-switching extension, we propose that our model could be used for Swiss recession dating. Our model does not indicate a regime-switch following the removal of the Swiss Franc lower bound. AbstractWe develop a small-scale dynamic factor model for the Swiss economy based on an appropriately selected set of indicators. The resulting business cycle factor is in striking accordance with historical Swiss business cycle fluctuations. Our proposed model demonstrates a remarkable performance in short-term and mediumterm forecasting. Using real-time GDP data since 2004, the model successfully anticipated the downturn of 2008-09 and responded in a timely manner to the recent sudden drop following the removal of the Swiss Franc lower bound. In a Markovswitching extension, we propose that our model could be used for Swiss recession dating. Our model does not indicate a regime-switch following the removal of the Swiss Franc lower bound.JEL class: C32, C53, E37 for valuable comments. P. Wegmueller would like to thank Ronald Indergand for support. Thanks also goes to seminar participants at SECO, SNB, STATEC/Luxembourg and the SSES conference for valuable comments. All errors are our own responsibility. The views expressed in this paper are those of the authors and do not necessarily represent those of the Swiss State Secretariat for Economic Affairs (SECO).

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Section: Additional Variablesmentioning

confidence: 99%

“…We make use of the realtime dataset from Indergand and Leist (2014) to construct two data vintages per month starting Republic, Canada, etc. (Chernis and Sekkel, 2017;Perez-Quiros, 2010, 2011;Camacho, Dal Bianco, and Martinez-Martin, 2015;Rusnák, 2016;Marcellino, Porqueddu, and Venditti, 2016).…”

Section: Introductionmentioning

confidence: 99%

Business Cycle Dating and Forecasting with Real-Time Swiss GDP Data

Glocker

Wegmueller²

2019

SSRN Journal

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“…In monitoring the global economic cycle the monthly IPI has ranked among the most important coincident indicators with the quarterly GDP [29]. IPI is not suitable as a main indicator tracking the cyclical development of the economy in all countries [10].…”

Section: Index Of Industrial Production (Ipi)mentioning

confidence: 99%

The Composite Coincident Indicator (CCI) for Business Cycles

2018

APH

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The aim of this paper is to introduce a composite coincident indicator for the Slovak economic cycle. The theoretical part of the work is concentrated on the analysis of current monitoring options of economic cycle using GDP, industrial production index and composite indicators. The analytical part of the work pays attention to authors' own composite coincident indicator and to testing its monitoring capabilities. Methodology used in this paper is a combination of the methodologies by the OECD, Conference Board and the authors of this paper. Such methods as seasonally adjusted time series, Hodrick-Prescott filter, standardization and cross-correlations are used as applied to 134 indicators of Slovak economy. Coincident cyclic indicators are selected from them, as they form the basis for Slovak composite coincident indicator. The resulting coincident composite indicator for Slovakia is made up of four indicatorsemployment in manufacturing, export of goods and services, the number of hours worked in industry and industrial turnover. The assembled indicator is then tested over time and a need for change in its composition as a result of significant economic changes is grounded. Significant changes in the composition of coincident cyclical indicator for 2010 are also proved. For the period of 2010-2015 a new composite coincident indicator is suggested of the composition of production index in the construction industry, a factor limiting production -insufficient demand, unemployment in the age group of 25-74 y.o. and short-term interest rate.

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“…We make use of the realtime dataset from Indergand and Leist (2014) to construct two data vintages per month starting Republic, Canada, etc. (Chernis and Sekkel, 2017;Perez-Quiros, 2010, 2011;Camacho, Dal Bianco, and Martinez-Martin, 2015; Rusnák, 2016; Marcellino, Porqueddu, and Venditti, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…There is evidence of shifts of the relationship between hard and soft indicators for the Euro zone as a whole as well as individual countries, sectors, and specific survey questions (see for instance(European Commission, 2016;Bruno, Crosilla, and Margani, 2016;de Largentaye and Roucher, 2015;Ferrara, Guégan, and Rakotomarolahy, 2010;Commission, 2011)). 25 See for instanceCamacho and Perez-Quiros (2010);Camacho, Dal Bianco, and Martinez-Martin (2015); Camacho and Perez-Quiros (2011); Rusnák (2016);Barnett, Chauvet, and Leiva-Leon (2014);Luciani and Ricci (2014);Fenz and Spitzer (2006);Galli, Hepenstrick, and Scheufele (2017).…”

mentioning

confidence: 99%