Catherine Doz

PSE Emeritus professor and Measurement in Economics Chair holder

RePEc page
  • Professor
  • Université Paris 1 Panthéon-Sorbonne
Research groups
  • Associate researcher at the International Macroeconomics Chair and at the Measurement in Economics Chair.
Research themes
  • Cycle
  • Macroeconometrics
Contact

Address :48 Boulevard Jourdan,
75014 Paris, France

Campus: Campus Jourdan
Floor: 4
Office: 10
Declaration of interest
See the declaration of interest

Tabs

Professor PSE and Université Paris 1

  • “Identifying and interpreting the factors in factor models via sparsity: Different approaches”, with Thomas Despois, Journal of Applied Econometrics 2023, vol.38(4), pp  533–555, http://dx.doi.org/10.1002/jae.2967
  • “Business Cycle Dynamics after the Great Recession“, PSE WP, 2020, with Laurent Ferrara and Pierre-Alain Pionnier. 
  • “Dynamic Factor Models“, in Macroeconomic Forecasting in the Era of Big Data, Theory and Practise, Peter Fuleky ed., Springer, 2020, with Peter Fuleky. 
  • “Forecasting French GDP with Dynamic Factor Models : a pseudo-real time experiment using Factor-augmented Error Correction Models“, PSE WP, 2018, with Stéphanie Combes.
  • “Dating Business Cycle Turning Points for the French Economy : an MS-DFM approach”, in Advances in Econometrics, vol 35, Dynamic Factor Models, 2016, with Anna Petronevich.
  • “Short term forecasting of French GDP growth using dynamic factor models”, OECD Journal : Journal of Business Cycle Measurement and Analysis, 2013(2), with Marie Bessec.
  • “Prévision de court terme de la croissance du PIB français à l’aide de modèles à facteurs dynamiques”, Economie et Prévision, 2012, with Marie Bessec
  • “A Quasi-Maximum Likelihood Approach for Large Approximate Dynamic Factor Models”, Review of Economics and Statistics, 2012, vol 94(4), pp.1014-1024, with Domenico Giannone and Lucrezia Reichlin.
  • “A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering”, Journal of Econometrics, 2011, vol. 164, pp. 188-205, 2011, with Domenico Giannone and Lucrezia Reichlin.
  • “Factor Stochastic Volatility in Mean Models : a GMM approach”, Econometric Reviews, 2006, vol. 25(2/3), pp. 275-309, with Eric Renault.
  • “Deux indicateurs synthétiques de l’activité industrielle dans la zone euro”, Note de Conjoncture de l’INSEE (Dossier), Juin 2000, with Fabrice Lenglart and Pascal Rivière.
  • “Modèles à facteurs dynamiques : test du nombre de facteurs, estimation, et application à l’enquête de conjoncture dans l’industrie”, Annales d’Economie et Statistique, 1999, with Fabrice Lenglart.
  • “Une grille de lecture pour l’enquête mensuelle dans l’industrie”, Note de Conjoncture de l’INSEE (Dossier), Décembre 1995, with Fabrice Lenglart.
  • “Décompositions tendance-cycle : estimation par des méthodes statistiques univariées”, Economie et Prévision, 1995, with Guillaume Rabault and Nicolas Sobczak.
  • “Décompositions tendance-cycle : les méthodes statistiques univariées” in  Un premier bilan du dernier cycle, Commission des Comptes et des Budgets Economiques de la Nation, 1994, with Nicolas Sobczak.
  • Note sur les tests de rationalité des prévisions, Economie et Prévision, 1993.
  • Modèles VAR et prévisions à court terme, Economie et Prévision, 1992, with Pierre Malgrange.
  • Vingt ans de prévisions macroéconomiques : une évaluation sur données françaises, Economie et Prévision, 1991, with Didier Borowski, Carine Bouthevillain, Pierre Malgrange, Pierre Morin.

  • Advanced Macroeconometrics : APE M2, with Laurent Ferrara. See APE website
  • Econométrie avancée des modèles linéaires : M1 Econométrie et Statistiques. Voir EPI Paris 1
  • Econometrics 2 : APE M1, with Melika Bensalem. See APE website.
  • Introduction à l’économétrie : L3 Economie. Voir EPI Paris 1.
  • Macroeconometrics : ENSAE and MiE. See ENSAE website.

Publications HAL

  • La datation des cycles par le CDCEF : résultats des approches économétriques Book section

    Après avoir tiré les leçons de l’histoire des cycles économiques avant et après Keynes, les aspects méthodologiques relatifs à la mesure et à la modélisation des cycles font l’objet d’une description approfondie. Se centrant ensuite sur la France, l’ouvrage propose une détermination des dates des phases de récession et d’expansion de l’économie française depuis 1970. La méthodologie retenue est originale, mêlant approches économétriques et narrative, et permet d’obtenir une datation précise des points de retournement du cycle économique français.
    Author: Claude Diebolt Editor: Economica

    Published in

  • La datation des cycles économiques français : une revue de la littérature Book section

    Après avoir tiré les leçons de l’histoire des cycles économiques avant et après Keynes, les aspects méthodologiques relatifs à la mesure et à la modélisation des cycles font l’objet d’une description approfondie. Se centrant ensuite sur la France, l’ouvrage propose une détermination des dates des phases de récession et d’expansion de l’économie française depuis 1970. La méthodologie retenue est originale, mêlant approches économétriques et narrative, et permet d’obtenir une datation précise des points de retournement du cycle économique français.
    Editor: Economica

    Published in

  • Identifying and interpreting the factors in factor models via sparsity: Different approaches Journal article

    With the usual estimation methods of factor models, the estimated factors are notoriously difficult to interpret, unless their interpretation is imposed via restrictions. This paper considers different methods to identify the factor structure and interpret the factors without imposing their interpretation: sparse PCA and factor rotations. We establish a new consistency result for the factors estimated by sparse PCA. Monte Carlo simulations show that our exploratory methods accurately estimate the factor structure, even in small samples. We also apply them on two standard large datasets about international business cycles and the US economy: for each empirical application, they identify the same factor structure, offering a clear economic interpretation of the estimated factors. These exploratory methods can be useful to justify or complement approaches in which the factor structure is imposed a priori.
    Journal: Journal of Applied Econometrics

    Published in

  • Dating business cycles in France : a reference chronology Journal article

    This paper proposes a reference quarterly chronology for periods of expansion and recession in France since 1970, carried out by the Dating Committee of the French Economic Association. The methodology is based on two pillars: 1) econometric estimations from various key data to identify candidate periods, and 2) a narrative approach that describes the economic background that prevailed at that time to finalize the dating chronology. Starting from 1970, the Committee has identified four economic recession periods: the two oil shocks 1974-1975 and 1980, the investment cycle of 1992-1993, and the Great Recession 2008-2009. For the Covid recession, the peak is dated in the last quarter of 2019 and the trough in the second quarter of 2020.
    Journal: Revue Economique

    Published in

  • Identifying and interpreting the factors in factor models via sparsity : Different approaches Pre-print, Working paper

    With the usual estimation methods of factor models, the estimated factors are notoriously difficult to interpret, unless their interpretation is imposed via restrictions. This paper considers different approaches for identifying the factor structure and interpreting the factors without imposing their interpretation: sparse PCA and factor rotations. We establish a new consistency result for the factors estimated by sparse PCA. Monte Carlo simulations show that our exploratory methods accurately estimate the factor structure, even in small samples. We also apply them to two standard large datasets about international business cycles and the US economy: for each empirical application, they identify the same factor structure, offering a clear economic interpretation of the estimated factors. These exploratory methods can justify or complement approaches which impose the factor structure a priori, and can also be useful for applications in which factor interpretation is usually overlooked.

    Published in

  • Identifying and interpreting the factors in factor models via sparsity: Different approaches Pre-print, Working paper

    With the usual estimation methods of factor models, the estimated factors are notoriously difficult to interpret, unless their interpretation is imposed via restrictions. This paper considers different methods to identify the factor structure and interpret the factors without imposing their interpretation: sparse PCA and factor rotations. We establish a new consistency result for the factors estimated by sparse PCA. Monte Carlo simulations show that our exploratory methods accurately estimate the factor structure, even in small samples. We also apply them on two standard large datasets about international business cycles and the US economy: for each empirical application, they identify the same factor structure, offering a clear economic interpretation of the estimated factors. These exploratory methods can be useful to justify or complement approaches in which the factor structure is imposed a priori.

    Published in

  • Dynamic Factor Models Book section

    Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.
    Editor: Springer

    Published in

  • Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model Pre-print, Working paper

    The Great Recession and the subsequent period of subdued GDP growth in most advanced economies have highlighted the need for macroeconomic forecasters to account for sudden and deep recessions, periods of higher macroeconomic volatility, and fluctuations in trend GDP growth. In this paper, we put forward an extension of the standard Markov-Switching Dynamic Factor Model (MS-DFM) by incorporating two new features: switches in volatility and time-variation in trend GDP growth. First, we show that volatility switches largely improve the detection of business cycle turning points in the low-volatility environment prevailing since the mid-1980s. It is an important result for the detection of future recessions since, according to our model, the US economy is now back to a low-volatility environment after an interruption during the Great Recession. Second, our model also captures a continuous decline in the US trend GDP growth that started a few years before the Great Recession and continued thereafter. These two extensions of the standard MS-DFM framework are supported by information criteria, marginal likelihood comparisons and improved real-time GDP forecasting performance.

    Published in

  • Dynamic Factor Models Pre-print, Working paper

    Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.

    Published in

  • Forecasting French GDP with Dynamic Factor Models : a pseudo-real time experiment using Factor-augmented Error Correction Models Pre-print, Working paper

    Dynamic Factor Models (DFMs) allow to take advantage of the information provided by a large dataset, which is summarized by a small set of unobservable latent variables, and they have proved to be very useful for short-term forecasting. Since most of their properties rely on the stationarity of the series, these models have been mainly used on data which have been di_erenciated to achieve stationarity. However estimation procedures for DFMs with I(1) common factors have been proposed by Bai (2004) and Bai and Ng(2004). Further, Banerjee and Marcellino (2008) and Banerjee, Marcellino and Masten (2014) have proposed to extend stationary Factor Augmented VAR models to the non-stationary case, and introduced Factor augmented Error Correction Models (FECM). We rely on this approach and conduct a pseudoreal time forecasting experiment, in which we compare short term forecasts of French GDP based on stationary and non-stationary DFMs. We mimic the timeliness of data, and use in the non-stationary framework the 2-step estimator proposed by Doz, Giannone and Reichlin(2011). In our study, forecasts based on stationary or non-stationary DFMs have a similar precision.

    Published in

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