A real-time recession indicator for the Euro area

In this paper, we propose a new coincident monthly indicator to detect in real-time the start and the end of an economic recession phase for the Euro area. In this respect, we use the methodology proposed in Anas and Ferrara (2002, 2004) as regards the recession indicator for the US, based on Markov-Switching processes popularized in economics by Hamilton (1989). By using a set of four monthly time series, we show that this start-end recession indicator (SERI) is able to reproduce all the recession phases experienced by the Euro area since 1970. Concerning the last low phase of the growth cycle in the Euro area, started in 2001, empirical results show that the Euro area experienced a « quasi-recession » phase, located between the end of the 2001 year and the beginning of 2002, without a global recession. This is due to a lack of diffusion of this phenomena among the main Eurozone countries, though it was synchronized.Recession; real-time; probabilistic indicator; Euro area

Real-time detection of the business cycle using SETAR models

We consider a threshold time series model in order to take into account some stylized facts of the business cycle such as asymmetries in the phases. Our aim is to point out some thresholds under (over) which a signal of turning point could be given. First, we introduce the various threshold models and we discuss both their statistical theoretical and empirical properties. Specifically, we review the classical techniques to estimate the number of regimes, the threshold, the delay and the parameters of the model. Then, we apply these models to the euro area industrial production index to detect, through a dynamic simulation approach, the dates of peaks and thoughs in business cycle.Economic cycle – Turning point detection Threshold model – Euro area IPI

Fractional seasonality: Models and Application to Economic Activity in the Euro Area

In this paper, we recall some concepts on seasonal long memory, we review the diverse fractionally integrated seasonal time series models and we discuss their statistical properties. Then, we compare the empirical performances of those models on euro area economic data and we show that generalized long memory models offer competitive alternatives to classical SARIMA models, avoiding over-differentiation and providing a better goodness of fit.Fractional seasonality, long-range dependence, generalized long memory models, economic activity.

Business surveys modelling with Seasonal-Cyclical Long Memory models

Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the economy, which is of great interest for policy-makers. In this paper, we focus on non-seasonally adjusted business surveys released by the European Commission. We introduce an innovative way for modelling those series taking the persistence of the seasonal roots into account through seasonal-cyclical long memory models. We empirically prove that such models produce more accurate forecasts than classical seasonal linear models.business surveys, seasonality, long memory models, forecasting

A non-parametric method to nowcast the Euro Area IPI

Non-parametric methods have been empirically proved to be of great interest in the statistical literature in order to forecast stationary time series, but very few applications have been proposed in the econometrics literature. In this paper, our aim is to test whether non-parametric statistical procedures based on a Kernel method can improve classical linear models in order to nowcast the Euro area manufacturing industrial production index (IPI) by using business surveys released by the European Commission. Moreover, we consider the methodology based on bootstrap replications to estimate the confidence interval of the nowcasts.Non-parametric, Kernel, nowcasting, bootstrap, Euro area IPI.

The European Way Out of Recessions

This paper proposes a two-regime Bounce-Back Function augmented Self-Exciting Threshold AutoRegression (SETAR) which allows for various shapes of recoveries from the recession regime. It relies on the bounce-back effects first analyzed in a Markov-Switching setup by Kim, Morley and Piger [2005] and recently extended by Bec, Bouabdallah and Ferrara [2011a]. This approach is then applied to post-1973 quarterly growth rates of French, German, Italian, Spanish and Euro area real GDPs. Both the linear autoregression and the standard SETAR without bounce-back effect null hypotheses are strongly rejected against the Bounce-Back augmented SETAR alternative in all cases but Italy. The relevance of our proposed model is further assessed by the comparison of its short-term forecasting performances with the ones obtained from a linear autoregression and a standard SETAR. It turns out that the bounce-back models one-step ahead forecasts generally outperform the other ones, and particularly so during the last recovery period in 2009Q3-2010Q4.Threshold autoregression, bounce-back effects, asymmetric business cycles. JEL classification: E32, C22.

Testing fractional order of long memory processes : a Monte Carlo study

Testing the fractionally integrated order of seasonal and non-seasonal unit roots is quite important for the economic and financial time series modelling. In this paper, Robinson test (1994) is applied to various well-known long memory models. Via Monte Carlo experiments, we study and compare the performances of this test using several sample sizes.Long memory processes, test, Monte Carlo simulations.

Testing Fractional Order of Long Memory Processes: A Monte Carlo Study

Testing the fractionally integrated order of seasonal and nonseasonal unit roots is quite important for the economic and financial time series modeling. In this article, the widely used Robinson's (1994) test is applied to various well-known long memory models. Via Monte Carlo experiments, we study and compare the performances of this test using several sample sizes.Long memory processes – test – Monte Carlo simulations

GDP nowcasting with ragged-edge data: a semi-parametric modeling

This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone.euro area GDP • real-time nowcasting • forecasting • non-parametric methods

GDP nowcasting with ragged-edge data : A semi-parametric modelling

This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on radial basis function approaches, ti forecast the monthly variables involved in the parametric modelling of GDP using bridge equations. A real-time experience is carried out on Euro area vintage data in order to anticipate, with an advance ranging from six to one months, the GDP flash estimate for the whole zone.Euro area GDP, real-time nowcasting, forecasting, non-parametric models.