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.

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

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

Zero-inflated truncated generalized Pareto distribution for the analysis of radio audience data

Extreme value data with a high clump-at-zero occur in many domains. Moreover, it might happen that the observed data are either truncated below a given threshold and/or might not be reliable enough below that threshold because of the recording devices. These situations occur, in particular, with radio audience data measured using personal meters that record environmental noise every minute, that is then matched to one of the several radio programs. There are therefore genuine zeros for respondents not listening to the radio, but also zeros corresponding to real listeners for whom the match between the recorded noise and the radio program could not be achieved. Since radio audiences are important for radio broadcasters in order, for example, to determine advertisement price policies, possibly according to the type of audience at different time points, it is essential to be able to explain not only the probability of listening to a radio but also the average time spent listening to the radio by means of the characteristics of the listeners. In this paper we propose a generalized linear model for zero-inflated truncated Pareto distribution (ZITPo) that we use to fit audience radio data. Because it is based on the generalized Pareto distribution, the ZITPo model has nice properties such as model invariance to the choice of the threshold and from which a natural residual measure can be derived to assess the model fit to the data. From a general formulation of the most popular models for zero-inflated data, we derive our model by considering successively the truncated case, the generalized Pareto distribution and then the inclusion of covariates to explain the nonzero proportion of listeners and their average listening time. By means of simulations, we study the performance of the maximum likelihood estimator (and derived inference) and use the model to fully analyze the audience data of a radio station in a certain area of Switzerland.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS358 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

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

Estimating gender differences in access to jobs: females trapped at the bottom of the ladder

In this paper, we propose a job assignment model allowing for a gender difference in access to jobs. Males and females compete for the same job positions. They are primarily interested in the best-paid jobs. A structural relationship of the model can be used to empirically recover the probability ratio of females and males getting a given job position. As this ratio is allowed to vary with the rank of jobs in the wage distribution of positions, barriers in females' access to high-paid jobs can be detected and quantiffed. We estimate the gender relative probability of getting any given job position for full-time executives aged 40-45 in the private sector. This is done using an exhaustive French administrative dataset on wage bills. Our results show that the access to any job position is lower for females than for males. Also, females' access decreases with the rank of job positions in the wage distribution, which is consistent with females being faced with more barriers to high-paid jobs than to low-paid jobs. At the bottom of the wage distribution, the probability of females getting a job is 12% lower than the probability of males. The difference in probability is far larger at the top of the wage distribution and climbs to 50%.gender ; discrimination ; wages ; quantiles ; job assignment model ; glass ceiling

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.

Mechanical behavior of entangled fibers and entangled cross-linked fibers during compression

Entangled fibrous materials have been manufactured from different fibers: metallic fibers, glass fibers, and carbon fibers. Specimens have been produced with and without cross links between fibers. Cross-links have been achieved using epoxy spraying. The scope of this article is to analyze the mechanical behavior of these materials and to compare it with available models. The first part of this article deals with entangled fibrous materials without crosslink between fibers. Compression tests are detailed and test reproducibility is checked. In the second part, compression tests were performed on materials manufactured with cross-linked fibers. The specific mechanical behavior obtained is discussed