Publications des chercheurs de PSE

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.

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.

Cet article propose une datation trimestrielle de référence des périodes de récession et d’expansion de l’économie française depuis 1970, réalisée par le comité de datation des cycles de l’Association française de science économique. La méthodologie repose sur deux piliers : 1) des estimations économétriques pour identifier les périodes candidates et 2) une approche narrative détaillant le contexte économique de l’époque pour finaliser la datation. De 1970 à 2019, quatre périodes de récession économique sont identifiées : les chocs pétroliers de 1974-1975 et 1980, le cycle d’investissement de 1992-1993 et la grande récession de 2008-2009. Pour la récession Covid, le pic est daté au dernier trimestre 2019 et le creux au deuxième trimestre 2020.

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.

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.

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.

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.

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.

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.

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.

The Markov-Switching Dynamic Factor Model (MS-DFM) has been used in different applications, notably in the business cycle analysis. When the cross-sectional dimension of data is high, the Maximum Likelihood estimation becomes unfeasible due to the excessive number of parameters. In this case, the MS-DFM can be estimated in two steps, which means that in the first step the common factor is extracted from a database of indicators, and in the second step the Markov-Switching autoregressive model is fit to this extracted factor. The validity of the two-step method is conventionally accepted, although the asymptotic properties of the two-step estimates have not been studied yet. In this paper we examine their consistency as well as the small-sample behavior with the help of Monte Carlo simulations. Our results indicate that the two-step estimates are consistent when the number of cross-section series and time observations is large, however, as expected, the estimates and their standard errors tend to be biased in small samples.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.