Photo Laurent Ferrara

Laurent Ferrara

Chargé(e) de recherches
  • Email
  • Tél. professionnel 0140977817
  • Bureau à Paris Nanterre (Bât. + num.) G517A
  • Axe de recherche

      Macroéconomie Internationale, Banque et Econométrie Financière

  • Thème(s)
    • Econométrie non-linéaire
    • Prévision macroéconomique
    • Cycles économiques
    • Economie internationale

2020-11 "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage"

Laurent Ferrara, Anna Simoni

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Résumé
Nous cherchons à savoir si, et quand, un grand ensemble de données de recherche Google peut être utile pour améliorer l'évaluation en temps réel du PIB, lorsque nous contrôlons par l'information présente dans les variables officielles. Pour cela, nous proposons une nouvelle approche qui combine la pré-sélection de variables et une méthode de régularisation Ridge, et nous donnons les résultats théoriques sur le comportement asymptotique de l'estimateur dans ce contexte. Les résultats empiriques pour la zone euro montrent que les données Google contiennent une information utile pour l'évaluation de la croissance du PIB dans des conditions de pseudo-temps-réel pour les quatre premières semaines du trimestre, c'est à dire lorsque l'information macroéconomique sur le trimestre en cours est manquante. Toutefois, leur capacité prédictive relative disparaît dès que les données officielles deviennent disponibles. De plus, une analyse dans des vraies conditions de temps réel, à l'aide de données millésimées, confirment que les données de recherche Google constituent une alternative crédible lorsque les données officielles sont manquantes.
Classification-JEL
C53, C55, E37
Mot(s) clé(s)
Evaluation en temps réel, PIB, large base de données, Google, Sélection de variables
Fichier

2018-2 "Global Financial interconnectedness: A non-linear assessment of the uncertainty channel"

Bertrand Candelon, Laurent Ferrara, Marc Joëts

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Résumé
Le rôle de l'incertitude dans l'économie globale est maintenant très largement reconnu au niveau académique et politique, mais ses effets sur le système financier international sont encore mal compris. Dans ce papier nous évaluons l'impact de l'incertitude sur les interconnexions entre les marchés financiers boursiers. Pour ce faire, nous étendons l'approche de Diebold and Yilmaz (2009) dans un cadre non linéaire par l'estimation d'un modèle VAR à seuils dans lequel les différents régimes dépendent du niveau d'incertitude. Nos résultats montrent que le niveau d'incertitude intensifie les phénomènes de contagion au sein des marchés boursiers de pays avancés et émergents. D'un point de vue de politique économique, ce résultat suggère qu'en présence d'incertitude, un choc financier négatif aura tendance à se propager plus intensément et plus largement à l'ensemble du système financier international. Ce résultat préconise un suivi en temps réel des mesures d'incertitude.
Classification-JEL
G15; C31; D84
Mot(s) clé(s)
Marchés financiers, réseau d'interconnexion, incertitude, modèle non linèaire
Fichier

2015-12 "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?"

Laurent Ferrara, Pierre Guérin

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Résumé
Following the Great Recession, econometric models that better account for un certainty have gained increased attention, and an increasing number of works evaluate the effects of uncertainty shocks. In this paper, we evaluate the impact of high-frequency uncertainty shocks on a set of low-frequency macroeconomic variables representative of the U.S. economy. Rather than estimating models at the same common low-frequency, we use recently developed econometric methodology that allows us to avoid aggregating high-frequency data before estimating models. The impulse response analysis uncovers various salient facts. First, in line with the existing literature, high-frequency uncertainty shocks are associated with a broad-based decline in economic activity. Second, we find that credit and labor market variables react the most to uncertainty shocks. Third, we show that the responses of macroeconomic variables to uncertainty shocks are relatively similar across single-frequency and mixed-frequency data models, suggesting that the temporal aggregation bias is not acute in this context. Finally, we find that some macroeconomic variables exhibit an asymmetric response to uncertainty shocks over the different phases of the business cycle.
Classification-JEL
E32, E44, C32.
Mot(s) clé(s)
MIDAS model, Mixed-frequency VAR, Uncertainty.
Fichier

2014-21 "Does the Great Recession imply the end of the Great Moderation? International evidence"

Amélie Charles, Olivier Darné, Laurent Ferrara

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Résumé
After years of low macroeconomic volatility since the early eighties, well documented and referred to as the Great Moderation period in the literature, the 2008-2009 worldwide recession adversely impacted output levels in most of advanced countries. This Great Recession period was characterized by a sharp apparent increase in output volatility. In this paper we evaluate whether this sudden event is likely to be temporary. Whether or not this new volatility regime is likely to persist would have strong macroeconomic effects, especially on business cycles. Based on break detection methods applied to a set of advanced countries, our empirical results do not give evidence to the end of the Great Moderation period but rather that the Great Recession is characterized by a dramatic temporary effect on the output growth but not on its volatility. In addition, we show that neglecting those breaks both in mean and in variance can have large effects on output volatility modelling. Last we empirically show that observed breaks during the Great Recession are to some extent related to uncertainty measures.
Classification-JEL
E32; C22
Mot(s) clé(s)
Great Recession; Great Moderation; breaks; volatility; uncertainty
Fichier

2013-19 "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?"

Laurent Ferrara, Clément Marsilli, Juan-Pablo Ortega

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Résumé
The Great Recession endured by the main industrialized countries during the period 2008-2009, in the wake of the financial and banking crisis, has pointed out the major role of the financial sector on macroeconomic fluctuations. In this paper, we reconsider macrofinancial linkages by assessing the leading role of the daily volatility of two major financial variables, namely commodity and stock prices, in their ability to anticipate US GDP growth. For this purpose, an extended MIDAS model is proposedthat allows the forecasting of the quarterly growth rate using exogenous variables sampled at various higher frequencies. Empirical results show that using both daily financial volatilities and monthly industrial production is helpful at the time of predicting quarterly GDP growth over the Great Recession period.
Classification-JEL
C53 E37
Mot(s) clé(s)
GDP forecasting; financial volatility; MIDAS approach
Fichier

2013-12 "Post-recession US employment through the lens of a non-linear Okun’s law"

Menzie Chinn, Laurent Ferrara, Valérie Mignon

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Résumé
This paper aims at investigating the relationship between employment and GDP in the United States. We disentangle trend and cyclical employment components by estimating a non-linear Okun’s law based on a smooth transition error-correction model that simultaneously accounts for long-term relationships between growth and employment and short-run instability over the business cycle. Our findings based on out-of-sample conditional forecasts show that, since the exit of the 2008-09 recession, US employment is on average around 1% below the level implied by the long run output-employment relationship, meaning that about 1.2 million of the trend employment loss cannot be attributed to the identified cyclical factors.
Classification-JEL
E24, E32, C22
Mot(s) clé(s)
Okun’s law, trend employment, non‐linear modeling
Fichier

2012-19 "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession"

Laurent Ferrara, Clément Marsilli

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Résumé
The global economic recession, referred to as the Great Recession, endured by the main industrialized countries during the period 2008-09, in the wake of the financial and banking crisis, has pointed out the current importance of the financial sector in macroeconomics. In this paper, we evaluate the predictive power of some major financial variables to anticipate GDP growth in euro area countries during this specific period of time. In this respect, we implement a MIDAS-based modeling approach, put forward by Ghysels et al. (2007), that enables to forecast quarterly GDP growth rates using exogenous variables sampled at higher frequencies. Empirical results show that, overall, stock prices help to improve the accuracy of GDP forecasts by comparison with a standard opinion survey variable, while oil prices and term spread appear to be less informative.
Classification-JEL
C2, C5, E3
Mot(s) clé(s)
Great Recession; Forecasting; Financial variables; MIDAS approach
Fichier

2011-27 "A new monthly chronology of the US industrial cycles in the prewar economy"

Amélie Charles, Olivier Darné, Claude Diebolt, Laurent Ferrara

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Résumé
This article extends earlier efforts at redating the US industrial cycles for the prewar period (1890–1938) using the methodologies proposed by Bry and Boschan (1971) and Hamilton (1989) and based on the monthly industrial production index constructed by Miron and Romer (1990). The alternative chronology detects 90% of the peaks and troughs identified by the NBER and Romer (1994), but the new dates are consistently dated earlier for more than 50% of them, especially as regards the NBER troughs. The new dates affect the comparison of the average duration of recessions and expansions in both pre-WWI and interwar eras. Whereas the NBER reference dates show an increase in average duration of the expansions between the pre-WWI and interwar periods, the new dates show evidence of shortened length of expansions. However, the new dates confirm the traditional finding that the length of contractions increases between the both eras.
Classification-JEL
C22; E32
Mot(s) clé(s)
Industrial business cycle; Dating chronology
Fichier

2010-14 "A factor-augmented probit model for business cycle analysis"

Christophe Bellégo, Laurent Ferrara

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Résumé
Dimension reduction of large data sets has been recently the topic of interest of many research papers dealing with macroeconomic modelling. Especially dynamic factor models have been proved to be useful for GDP nowcasting or short-term forecasting. In this paper, we put forward an innovative factor-augmented probit model in order to analyze the business cycle. Factor estimation is carried either by standard statistical methods or by allowing a richer dynamic behaviour. An application is provided on euro area data in order to point out the ability of the model to detect recessions over the period 1974-2008.
Classification-JEL
Mot(s) clé(s)
Fichier
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