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.

A non-parametric method to nowcast the Euro Area IPI

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/CESFramDP2008.htmDocuments de travail du Centre d'Economie de la Sorbonne 2008.33 - ISSN : 1955-611XNon-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.Les méthodes non-paramétriques ont été largement utilisées dans la littérature statistique afin de prévoir une série chronologique stationnaire, mais très peu d'applications ont été fournies en économétrie. Dans ce papier, nous développons une approche non-paramétrique basée sur la méthode des noyaux afin de prévoir l'indice de la production industrielle dans la zone euro à l'aide des enquêtes dans l'industrie de la Commission Européenne. Nous montrons que cette approche permet d'améliorer certaines méthodes de prévision linéaire plus classiques. De plus, nous estimons l'intervalle de confiance de la prévision à l'aide de réplications Bootstrap

Cycles économiques et gestion de portefeuille

A well-worked theory of macro-based investment decision is introduced. The theoretical influence of economic cycles on time-varying risk premiums is explained and exhibited. The importance of the turning points of the growth cycle, better known as the output gap, is outlined. To quickly and accurately detect economic turning points, probabilistic indicators are first created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization. Those indicators are robust, interpretable and preserve economic consistency. A more complex approach is then evaluated: ensemble machine learning algorithms, referred to as random forest and as boosting, are applied. The two key features of those algorithms are their abilities to entertain a large number of predictors and to perform estimation and variable selection simultaneously. With both approaches investment strategies based on the models achieve impressive risk-adjusted returns: timing the market is thus possible. At last, exploring a new way of capital allocation, a hierarchical clustering based asset allocation method is introduced. The empirical results indicate that hierarchical clustering based portfolios are robust, truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.Cette thèse cherche à lier les cycles économiques et la gestion de portefeuille. Le premier chapitre construit un cadre théorique entre les cycles économiques et les primes de risques. Il met en évidence l’importance des points de retournement du cycle de croissance, plus connu sous le nom d’écart de production. Les deux chapitres suivants ont pour objectif de détecter en temps réel ces points de retournement. La première approche se concentre sur une méthode non paramétrique d’apprentissage automatique simple et facilement compréhensible appelée quantification vectorielle adaptative. La seconde approche utilise des méthodes plus complexes d’apprentissage automatique, dites ensemblistes : les forêts aléatoires et le boosting. Les deux démarches permettent de créer des stratégies d’investissement performantes en temps réel. Enfin, le dernier chapitre élabore une méthode d’allocation d’actifs à partir de différents algorithmes de regroupement hiérarchique. Les résultats empiriques démontrent l’intérêt de cette tentative : les portefeuilles créés sont robustes, diversifiés et lucratifs

Asset Allocation, Economic Cycles and Machine Learning

Cette thèse cherche à lier les cycles économiques et la gestion de portefeuille. Le premier chapitre construit un cadre théorique entre les cycles économiques et les primes de risques. Il met en évidence l’importance des points de retournement du cycle de croissance, plus connu sous le nom d’écart de production. Les deux chapitres suivants ont pour objectif de détecter en temps réel ces points de retournement. La première approche se concentre sur une méthode non paramétrique d’apprentissage automatique simple et facilement compréhensible appelée quantification vectorielle adaptative. La seconde approche utilise des méthodes plus complexes d’apprentissage automatique, dites ensemblistes : les forêts aléatoires et le boosting. Les deux démarches permettent de créer des stratégies d’investissement performantes en temps réel. Enfin, le dernier chapitre élabore une méthode d’allocation d’actifs à partir de différents algorithmes de regroupement hiérarchique. Les résultats empiriques démontrent l’intérêt de cette tentative : les portefeuilles créés sont robustes, diversifiés et lucratifs.A well-worked theory of macro-based investment decision is introduced. The theoretical influence of economic cycles on time-varying risk premiums is explained and exhibited. The importance of the turning points of the growth cycle, better known as the output gap, is outlined. To quickly and accurately detect economic turning points, probabilistic indicators are first created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization. Those indicators are robust, interpretable and preserve economic consistency. A more complex approach is then evaluated: ensemble machine learning algorithms, referred to as random forest and as boosting, are applied. The two key features of those algorithms are their abilities to entertain a large number of predictors and to perform estimation and variable selection simultaneously. With both approaches investment strategies based on the models achieve impressive risk-adjusted returns: timing the market is thus possible. At last, exploring a new way of capital allocation, a hierarchical clustering based asset allocation method is introduced. The empirical results indicate that hierarchical clustering based portfolios are robust, truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques

A monthly indicator of GDP for Euro-Area based on business surveys

Policy-makers and analysts are continually assessing the state of the economy. But, gross domestic product (GDP) is only available on quarterly basis with a time span of 1-3 months, and sometimes with significant revisions. The aim of this article is to develop a monthly indicator of GDP for Euro-Area based on business surveys and to give the outlook of the economy activity induced by the indicator.

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.