1000 resultados para Processus linéaires
Resumo:
The study examines the signalling of text organisation in research articles (RA) in French. The work concentrates on a particular type of organisation provided by text sequences, i.e. structures organising text to items of which at least some are signalled by markers of addition or order: First… 0… The third point… In addition… / Premièrement… 0… Le troisième point… De plus… By indicating the way the text is organised, these structures guide the reader in the reading process so that he doesn’t need to interpret the text structure himself. The aim of the work is to study factors affecting the marking of text sequences. Why is their structure sometimes signalled explicitly by markers such as secondly, whereas in other places such markers are not used? The corpus is manually XML-annotated and consists of 90 RAs (~800 000 words) in French from the fields of linguistics, education and history. The analysis highlights several factors affecting the marking of text sequences. First, exact markers (such as fist ) seem to be more frequent in sequences where all the items are explicitly signalled by a marker, whereas additive markers (such as moreover) are used in sequences with both explicitly signalled and unmarked items. The marking of explicitly signalled sequences seems thus to be precise and even repetitive, whereas the signalling of sequences with unmarked items is altogether more vague. Second, the marking of text sequences seems to depend on the length of the text. The longer the text segment, the more vague the marking. Additive markers and unmarked items are more frequent in longer sequences possibly covering several pages, whereas shorter sequences are often signalled explicitly by exact markers. Also the marker types vary according to the sequence length. Anaphoric expressions, such as first, are fairly close to their referents and are used in short sequences, connectors, such as secondly, are frequently used in sequences of intermediate length, whereas the longest sequences are often signalled by constructions composed of an ordinal and a noun acting as a subject of the sentence: The first item is… Finally, the marking of text organisation depends also on the discipline the RA belongs to. In linguistics, the marking is fairly frequent and precise; exact markers such as second are the most used, and structures with unmarked items are less common. Similarly, the marking is fairly frequent in education. In this field, however, it is also less precise than in linguistics, with frequent unmarked items and additive markers. History, on the other hand, is characterised by less frequent marking. In addition, when used, the marking in this field is also less precise and less explicit.
Resumo:
Invokaatio: I.N.J.
Resumo:
Une seule main. La copie a été certifiée par deux notaires, "Dionisius Comitis" et "Franciscus Ferrebouc" qui ont signé au bas de chaque feuillet (180). Ce ms. fait partie d'une série d'expéditions pour lesquelles il existe des mentions de paiement datées de 1458. D'après P. Champion, il s'agit d'une copie authentique abrégée du procès de réhabilitation de Jeanne d'Arc. Deux autres copies sont connues: le ms. lat. 5970 de la BnF, certifié par les mêmes notaires, mais copié par des mains différentes, et le ms. Stowe 84, conservé à la British Library."
Resumo:
Colbertinus
Resumo:
In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.
Resumo:
In this paper, we introduce a new approach for volatility modeling in discrete and continuous time. We follow the stochastic volatility literature by assuming that the variance is a function of a state variable. However, instead of assuming that the loading function is ad hoc (e.g., exponential or affine), we assume that it is a linear combination of the eigenfunctions of the conditional expectation (resp. infinitesimal generator) operator associated to the state variable in discrete (resp. continuous) time. Special examples are the popular log-normal and square-root models where the eigenfunctions are the Hermite and Laguerre polynomials respectively. The eigenfunction approach has at least six advantages: i) it is general since any square integrable function may be written as a linear combination of the eigenfunctions; ii) the orthogonality of the eigenfunctions leads to the traditional interpretations of the linear principal components analysis; iii) the implied dynamics of the variance and squared return processes are ARMA and, hence, simple for forecasting and inference purposes; (iv) more importantly, this generates fat tails for the variance and returns processes; v) in contrast to popular models, the variance of the variance is a flexible function of the variance; vi) these models are closed under temporal aggregation.
Resumo:
This paper extends the Competitive Storage Model by incorporating prominent features of the production process and financial markets. A major limitation of this basic model is that it cannot successfully explain the degree of serial correlation observed in actual data. The proposed extensions build on the observation that in order to generate a high degree of price persistence, a model must incorporate features such that agents are willing to hold stocks more often than predicted by the basic model. We therefore allow unique characteristics of the production and trading mechanisms to provide the required incentives. Specifically, the proposed models introduce (i) gestation lags in production with heteroskedastic supply shocks, (ii) multiperiod forward contracts, and (iii) a convenience return to inventory holding. The rational expectations solutions for twelve commodities are numerically solved. Simulations are then employed to assess the effects of the above extensions on the time series properties of commodity prices. Results indicate that each of the features above partially account for the persistence and occasional spikes observed in actual data. Evidence is presented that the precautionary demand for stocks might play a substantial role in the dynamics of commodity prices.
Resumo:
This paper exploits the term structure of interest rates to develop testable economic restrictions on the joint process of long-term interest rates and inflation when the latter is subject to a targeting policy by the Central Bank. Two competing models that econometrically describe agents’ inferences about inflation targets are developed and shown to generate distinct predictions on the behavior of interest rates. In an empirical application to the Canadian inflation target zone, results indicate that agents perceive the band to be substantially narrower than officially announced and asymmetric around the stated mid-point. The latter result (i) suggests that the monetary authority attaches different weights to positive and negative deviations from the central target, and (ii) challenges on empirical grounds the assumption, frequently made in the literature, that the policy maker’s loss function is symmetric (usually a quadratic function) around a desired inflation value.
Resumo:
The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.
Resumo:
Ce texte propose des méthodes d’inférence exactes (tests et régions de confiance) sur des modèles de régression linéaires avec erreurs autocorrélées suivant un processus autorégressif d’ordre deux [AR(2)], qui peut être non stationnaire. L’approche proposée est une généralisation de celle décrite dans Dufour (1990) pour un modèle de régression avec erreurs AR(1) et comporte trois étapes. Premièrement, on construit une région de confiance exacte pour le vecteur des coefficients du processus autorégressif (φ). Cette région est obtenue par inversion de tests d’indépendance des erreurs sur une forme transformée du modèle contre des alternatives de dépendance aux délais un et deux. Deuxièmement, en exploitant la dualité entre tests et régions de confiance (inversion de tests), on détermine une région de confiance conjointe pour le vecteur φ et un vecteur d’intérêt M de combinaisons linéaires des coefficients de régression du modèle. Troisièmement, par une méthode de projection, on obtient des intervalles de confiance «marginaux» ainsi que des tests à bornes exacts pour les composantes de M. Ces méthodes sont appliquées à des modèles du stock de monnaie (M2) et du niveau des prix (indice implicite du PNB) américains
Resumo:
We propose methods for testing hypotheses of non-causality at various horizons, as defined in Dufour and Renault (1998, Econometrica). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the U.S. economy.
Resumo:
Rapport de recherche
