Structural change and long memory in the dynamic of U.S. inflation process

International audienceLong range dependence and regime switching are very intimately related effects. In this paper we consider the problem of spuriously detecting break dates in hypothesis of long memory data generating processes. For this purpose, we address the issue of estimating the number of breaks using several techniques, namely, the information criteria, Bai and Perron's sequential selection procedure (1998), and the automatic procedure of Lavielle (2004). By means of Monte Carlo experiments, we investigate the effect of increasing the long memory parameter on selecting the number of breaks and their locations, and show that the Lavielle's method is the best technique since its frequency of choosing the true number of changes is the highest particularly when the order of integration is close to 0.5. As it seems that inflation rates contains long memory and structural breaks, an application to the U.S. inflation process is presented to illustrate the usefulness of these procedures. The results show that the Lavielle's method (2004) selects only two breaks, however, the number of breaks detected by the information criteria and the sequential procedure of Bai and Perron (1998) are superior or equal to three

LE CHANGEMENT STRUCTUREL DANS UN ENVIRONNEMENT MÉMOIRE LONGUE

In this paper, we consider the problem of estimation of the break dates and present an efficient algorithm in order to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(n2) for any number of breaks. We also study the estimation of the number of breaks by using the information criteria, the test of Bai and Perron (1998), and the method of Lavielle (2004). Finally, we perform a Monte Carlo study to analyse the behaviour of estimators and tests infinite sample size.Dans ce papier, nous considérons le problème d'estimation des dates de rupture et présentons un algorithme efficace pour obtenir les minimums globaux des sommes des carrés des résidus. Cet algorithme est basé sur le principe de la programmation dynamique et nécessite au plus des opérations de moindres carrés d'ordre O(n2) pour tout nombre de ruptures. Ensuite nous abordons le problème d'estimation du nombre de ruptures par les critères d'information, le test de Bai et Perron (1998) et la méthode de Lavielle (2004). Finalement, une analyse par la méthode de Monte Carlo donnera une idée sur le comportement des estimateurs et des tests en échantillons finis

Analysing CPI inflation by the fractionally integrated ARFIMA-STVGARCH model

The aim of this paper is to study the dynamic evolution of inflation rate. The model is constructed by extending the ARFIMA-GARCH to ARFIMA with a time varying GARCH model where the transition from one regime to another is evolving smoothly over time. We show by Monte Carlo experiments that the constancy parameter tests perform well. We apply then this new model on eight countries from Europe, Japan and Canada and find that this model is appropriate for six among these countries.ARFIMA model, Generalised autoregressive conditional heteroscedasticity model, Inflation rate, Long memory process, Nonlinear time series, Time-varying parameter mode

LE CHANGEMENT STRUCTUREL DANS UN ENVIRONNEMENT MÉMOIRE LONGUE

Dans ce papier, nous considérons le problème d'estimation des dates de rupture et présentons un algorithme efficace pour obtenir les minimums globaux des sommes des carrés des résidus. Cet algorithme est basé sur le principe de la programmation dynamique et nécessite au plus des opérations de moindres carrés d'ordre O(n2) pour tout nombre de ruptures. Ensuite nous abordons le problème d'estimation du nombre de ruptures par les critères d'information, le test de Bai et Perron (1998) et la méthode de Lavielle (2004). Finalement, une analyse par la méthode de Monte Carlo donnera une idée sur le comportement des estimateurs et des tests en échantillons finis.Changement structurel pur, dates de ruptures, estimation des moindres carrés pénalisés, mémoire longue, programmation dynamique, régimes multiples, sélection de modèle, tests d'hypothèses.

Multidisciplinary and cosmopolitan: how openness influences the academic impact of a scholar's research

The academic impact of a scholar's research remains of great importance to institutions, particularly business schools. Hyungseok (David) Yoon and Mustapha Belkhouja report on research examining how scholars' openness to other disciplines and broader collaborations influences their academic impact, as determined by citation analysis. Findings suggest that the career stage of academics is an important factor, with early-career researchers encouraged to be open to multidisciplinary work (up to a tipping point) and to collaborating with academics from different institutions, while academics who are more established in their careers should also continue to diversify their research domains but ideally focus more on internal collaborations

Patterns of Knowledge Outflow from Industrial Marketing Management to Major Marketing and Specialized Journals (1999-2013): A Citation Analysis

International audienc

Modélisation des non linéarités dans des séries à mémoire longue : simulation et études empiriques

Cette thèse porte sur l'identification et l'estimation des ruptures structurelles pouvant affecter des données économiques et financières à mémoire longue. Notre étude s'est limitée dans les trois premiers chapitres au cadre univarié où nous avons modélisé la dépendance de long terme et les changements structurels simultanément et séparément au niveau de la moyenne ainsi que la volatilité. Dans un premier temps nous n'avons tenu compte que des sauts instantanés d'état ensuite nous nous sommes intéressés à la possibilité d'avoir des changements graduels et lisses au cours du temps grâce à des modèles nonlinéaires plus complexes. Par ailleurs, des expériences de simulation ont été menées dans le but d'offrir une analyse comparative des méthodes utilisées et d'attester de la robustesse des tests sous certaines conditions telle que la présence de la mémoire longue dans la série. Ce travail s'est achevé sur une extension aux modèles multivariés.Ces modèles permettent de rendre compte des mécanismes de propagation d'une variation d'une série sur l'autre et d'identifier les liens entre les variables ainsi que la nature des ces liens. Les interactions entre les différentes variables financières ont été analysées tant à court terme qu'à long terme. Bien que le concept du changement structurel n'a pas été abordé dans ce dernier chapitre, nous avons pris en compte l'effet d'asymétrie et de mémoire longue dans la modélisation de la volatilité.This dissertation deals with the detection and the estimation of structural changes in long memory economic and financial time series. Within the rest three chapters we focused on the univariate case to model both the long range dependence and structural changes in the mean and the volatility of the examined series. In the beginning we just take into account abrupt regime switches but after we use more developed nonlinear models in order to capture the smooth time variations of the dynamics. Otherwise we analyse the efficiency of various techniques permitting to select the number of breaks and we assess the robustness of the used tests in a long memory environment via simulations. Last, this thesis was completed by an extension to multivariate models. These models allow us to detect the impact of some series on the others and identify the relationships among them. The interdependencies between the financial variables were studied and analysed both in the short and the long range. While structural changes were not considered in the last chapter, our multivariate model takes into account asymmetry effects and the long memory behaviour in the volatility

Analysing CPI inflation by the fractionally integrated ARFIMA-STVGARCH model

The aim of this paper is to study the dynamic evolution of inflation rate. The model is constructed by extending the ARFIMA-GARCH to ARFIMA with a time varying GARCH model where the transition from one regime to another is evolving smoothly over time. We show by Monte Carlo experiments that the constancy parameter tests perform well. We apply then this new model on eight countries from Europe, Japan and Canada and find that this model is appropriate for six among these countries

Modeling volatility with time-varying FIGARCH models

This paper puts the light on a new class of time-varying FIGARCH or TV-FIGARCH processes to model the volatility. This new model has the feature to account for the long memory and the structural change in the conditional variance process. The structural change is modeled by a logistic function allowing the intercept to vary over time. We also implement a modeling strategy for our TV-FIGARCH specification whose performance is examined by a Monte Carlo study. An empirical application to the crude oil price and the S&P 500 index is carried out to illustrate the usefulness of our techniques. The main result of this paper is that the long memory behavior of the absolute returns is not only explained by the existence of the long memory in the volatility but also by deterministic changes in the unconditional variance.FIGARCH Long memory Nonlinear time series Structural change Time-varying parameter model