Distribution-Free Bounds for Serial Correlation Coefficients in Heteroskedastic Symmetric Time Series

We consider the problem of testing whether the observations X1, ..., Xn of a time series are independent with unspecified (possibly nonidentical) distributions symmetric about a common known median. Various bounds on the distributions of serial correlation coefficients are proposed: exponential bounds, Eaton-type bounds, Chebyshev bounds and Berry-Esséen-Zolotarev bounds. The bounds are exact in finite samples, distribution-free and easy to compute. The performance of the bounds is evaluated and compared with traditional serial dependence tests in a simulation experiment. The procedures proposed are applied to U.S. data on interest rates (commercial paper rate).

Lecciones de cosas : en series graduadas, con nociones de objetos comunes : E. A. Sheldon

UANL

L'estimation de modeles de series chronologiques avec erreur sur les variables.

Rapport de recherche

Compendio de Matemáticas puras y mistas

UANL

[Tratado de matemáticas]

UANL

Elementos de matemáticas

UANL

Kings of the battle-field: comprising a series of biographical sketches of the most distinguished military leaders...

UANL

Polyglot reader, and guide for traslation: consisting of a series of english extracts, with their traslation into French, German, Spanish, and Italian : the severeal parts designed to serve as mu tual keys

UANL

Pronóstico de series temporales con estacionalidad.

Tesis (Doctor en Ingeniería con Especialidad en Ingeniería de Sistemas) UANL, 2011.

Beyond Panel Unit Root Tests: Using Multiple Testing to Determine the Non Stationarity Properties of Individual Series in a Panel

Most panel unit root tests are designed to test the joint null hypothesis of a unit root for each individual series in a panel. After a rejection, it will often be of interest to identify which series can be deemed to be stationary and which series can be deemed nonstationary. Researchers will sometimes carry out this classification on the basis of n individual (univariate) unit root tests based on some ad hoc significance level. In this paper, we demonstrate how to use the false discovery rate (FDR) in evaluating I(1)=I(0) classifications based on individual unit root tests when the size of the cross section (n) and time series (T) dimensions are large. We report results from a simulation experiment and illustrate the methods on two data sets.