Home  About us  Contact
 

Finite-sample Performance (finite-sample + performance)

Distribution by Scientific Domains
Mathematics and Statistics66%
Business, Economics, Finance and Accounting33%

Selected Abstracts

ADL tests for threshold cointegration

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2010
Jing Li
C12; C15; C32 In this article, we propose new tests for threshold cointegration using an autoregressive distributed lag (ADL) model. The indicators in the threshold model can adopt either a nonstationary or stationary threshold variable. The cointegrating vector is not prespecified in this article. We adopt a supremum Wald type test to account for the so-called Davies (1987, Biometrika 74,33) problem. The asymptotic null distributions of the proposed tests are free of nuisance parameters. As such, a bootstrap procedure is not required and the critical values of the proposed tests are tabulated. Monte Carlo experiments show good finite-sample performance. [source]

On testing for multivariate ARCH effects in vector time series models

THE CANADIAN JOURNAL OF STATISTICS, Issue 3 2003
Pierre Duchesne
Abstract Using a spectral approach, the authors propose tests to detect multivariate ARCH effects in the residuals from a multivariate regression model. The tests are based on a comparison, via a quadratic norm, between the uniform density and a kernel-based spectral density estimator of the squared residuals and cross products of residuals. The proposed tests are consistent under an arbitrary fixed alternative. The authors present a new application of the test due to Hosking (1980) which is seen to be a special case of their approach involving the truncated uniform kernel. However, they typically obtain more powerful procedures when using a different weighting. The authors consider especially the procedure of Robinson (1991) for choosing the smoothing parameter of the spectral density estimator. They also introduce a generalized version of the test for ARCH effects due to Ling & Li (1997). They investigate the finite-sample performance of their tests and compare them to existing tests including those of Ling & Li (1997) and the residual-based diagnostics of Tse (2002).Finally, they present a financial application. Adoptant une approche spectrale, les auteurs proposent des tests permettant de détecter des effets ARCH multivariés dans les résidus d'un modèle de régression multivarié. Leurs tests reposent sur une comparaison en norme quadratique de la densité spectrale uniforme et d'un estimateur à noyau de la densité spectrale des résidus carrés et des produits croisés des résidus. Ces tests sont convergents sous une contre-hypothèse fixe quelconque. Les auteurs présentent une nouvelle application du test de Hosking (1980) qui correspond dans leur approche au choix particulier d'un noyau uniforme tronqué. Cependant, l'emploi d'autres pondérations leur permet d'obtenir des test encore plus puissants. Les auteurs étudient notamment la procédure de Robinson (1991) pour le choix du paramètre de lissage de l'estimateur de la densité spectrale. Os proposent aussi une version généralisée du test pour effets ARCH de Ling & Li (1997). Ils examinent le comportement de leurs tests dans de petits échantillons par voie de simulation et les comparent aux tests de Ling & Li (1997) et aux diagnostiques de Tse (2002) fondés sur les résidus, us présentent en outre une application financière. [source]

Goodness-of-fit tests for functional data

THE ECONOMETRICS JOURNAL, Issue 2009
Federico A. Bugni
Summary, Economic data are frequently generated by stochastic processes that can be modelled as occurring in continuous time. That is, the data are treated as realizations of a random function (functional data). Sometimes an economic theory model specifies the process up to a finite-dimensional parameter. This paper develops a test of the null hypothesis that a given functional data set was generated by a specified parametric model of a continuous-time process. The alternative hypothesis is non-parametric. A random function is a form of infinite-dimensional random variable, and the test presented here a generalization of the familiar Cramér-von Mises test to an infinite dimensional random variable. The test is illustrated by using it to test the hypothesis that a sample of wage paths was generated by a certain equilibrium job search model. Simulation studies show that the test has good finite-sample performance. [source]

A Gaussian approach for continuous time models of the short-term interest rate

THE ECONOMETRICS JOURNAL, Issue 2 2001
Jun Yu
This paper proposes a Gaussian estimator for nonlinear continuous time models of the short-term interest rate. The approach is based on a stopping time argument that produces a normalizing transformation facilitating the use of a Gaussian likelihood. A Monte Carlo study shows that the finite-sample performance of the proposed procedure offers an improvement over the discrete approximation method proposed by Nowman (1997). An empirical application to US and British interest rates is given. [source]

PENALIZED- R2 CRITERIA FOR MODEL SELECTION,

THE MANCHESTER SCHOOL, Issue 6 2009
LARRY W. TAYLOR
It is beneficial to observe that popular model selection criteria for the linear model are equivalent to penalized versions of R2. Let PR2 refer to any one of these model selection criteria. Then PR2 serves the dual purpose of selecting the model and summarizing the resulting fit subject to the penalty function. Furthermore, it is straightforward to extend the logic of PR2 to instrumental variables estimation and the non-parametric selection of regressors. For two-stage least squares estimation, a simulation study investigates the finite-sample performance of PR2 to select the correct model in cases of either strong or weak instruments. [source]

ERROR VARIANCE ESTIMATION FOR THE SINGLE-INDEX MODEL

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2010
K. B. Kulasekera
Summary Single-index models provide one way of reducing the dimension in regression analysis. The statistical literature has focused mainly on estimating the index coefficients, the mean function, and their asymptotic properties. For accurate statistical inference it is equally important to estimate the error variance of these models. We examine two estimators of the error variance in a single-index model and compare them with a few competing estimators with respect to their corresponding asymptotic properties. Using a simulation study, we evaluate the finite-sample performance of our estimators against their competitors. [source]

Incorporating Predictor Network in Penalized Regression with Application to Microarray Data

BIOMETRICS, Issue 2 2010
Wei Pan
Summary We consider penalized linear regression, especially for "large,p, small,n" problems, for which the relationships among predictors are described a priori by a network. A class of motivating examples includes modeling a phenotype through gene expression profiles while accounting for coordinated functioning of genes in the form of biological pathways or networks. To incorporate the prior knowledge of the similar effect sizes of neighboring predictors in a network, we propose a grouped penalty based on the,L, -norm that smoothes the regression coefficients of the predictors over the network. The main feature of the proposed method is its ability to automatically realize grouped variable selection and exploit grouping effects. We also discuss effects of the choices of the , and some weights inside the,L, -norm. Simulation studies demonstrate the superior finite-sample performance of the proposed method as compared to Lasso, elastic net, and a recently proposed network-based method. The new method performs best in variable selection across all simulation set-ups considered. For illustration, the method is applied to a microarray dataset to predict survival times for some glioblastoma patients using a gene expression dataset and a gene network compiled from some Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. [source]

Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data

BIOMETRICS, Issue 3 2009
Wenqin Pan
Summary We consider semiparametric transition measurement error models for longitudinal data, where one of the covariates is measured with error in transition models, and no distributional assumption is made for the underlying unobserved covariate. An estimating equation approach based on the pseudo conditional score method is proposed. We show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also discuss the issue of efficiency loss. Simulation studies are conducted to examine the finite-sample performance of our estimators. The longitudinal AIDS Costs and Services Utilization Survey data are analyzed for illustration. [source]

Measurement Error in a Random Walk Model with Applications to Population Dynamics

BIOMETRICS, Issue 4 2006
John Staudenmayer
Summary Population abundances are rarely, if ever, known. Instead, they are estimated with some amount of uncertainty. The resulting measurement error has its consequences on subsequent analyses that model population dynamics and estimate probabilities about abundances at future points in time. This article addresses some outstanding questions on the consequences of measurement error in one such dynamic model, the random walk with drift model, and proposes some new ways to correct for measurement error. We present a broad and realistic class of measurement error models that allows both heteroskedasticity and possible correlation in the measurement errors, and we provide analytical results about the biases of estimators that ignore the measurement error. Our new estimators include both method of moments estimators and "pseudo"-estimators that proceed from both observed estimates of population abundance and estimates of parameters in the measurement error model. We derive the asymptotic properties of our methods and existing methods, and we compare their finite-sample performance with a simulation experiment. We also examine the practical implications of the methods by using them to analyze two existing population dynamics data sets. [source]