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Finite-sample Properties (finite-sample + property)

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

Selected Abstracts

Finite sample improvements in statistical inference with I(1) processes

JOURNAL OF APPLIED ECONOMETRICS, Issue 3 2001
D. Marinucci
Robinson and Marinucci (1998) investigated the asymptotic behaviour of a narrow-band semiparametric procedure termed Frequency Domain Least Squares (FDLS) in the broad context of fractional cointegration analysis. Here we restrict discussion to the standard case when the data are I(1) and the cointegrating errors are I(0), proving that modifications of the Fully Modified Ordinary Least Squares (FM-OLS) procedure of Phillips and Hansen (1990) which use the FDLS idea have the same asymptotically desirable properties as FM-OLS, and, on the basis of a Monte Carlo study, find evidence that they have superior finite-sample properties. The new procedures are also shown to compare satisfactorily with parametric estimates. Copyright © 2001 John Wiley & Sons, Ltd. [source]

The Effect of the Estimation on Goodness-of-Fit Tests in Time Series Models

JOURNAL OF TIME SERIES ANALYSIS, Issue 4 2005
Yue Fang
Abstract., We analyze, by simulation, the finite-sample properties of goodness-of-fit tests based on residual autocorrelation coefficients (simple and partial) obtained using different estimators frequently used in the analysis of autoregressive moving-average time-series models. The estimators considered are unconditional least squares, maximum likelihood and conditional least squares. The results suggest that although the tests based on these estimators are asymptotically equivalent for particular models and parameter values, their sampling properties for samples of the size commonly found in economic applications can differ substantially, because of differences in both finite-sample estimation efficiencies and residual regeneration methods. [source]

Blockwise empirical entropy tests for time series regressions

JOURNAL OF TIME SERIES ANALYSIS, Issue 2 2005
Francesco Bravo
Abstract., This paper shows how the empirical entropy (also known as exponential likelihood or non-parametric tilting) method can be used to test general parametric hypothesis in time series regressions. To capture the weak dependence of the observations, the paper uses blocking techniques which are also used in the bootstrap literature on time series. Monte Carlo evidence suggests that the proposed test statistics have better finite-sample properties than conventional test statistics such as the Wald statistic. [source]

Detection of Structural Change in the Long-run Persistence in a Univariate Time Series,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 2 2005
Eiji Kurozumi
Abstract In this paper, we investigate a test for structural change in the long-run persistence in a univariate time series. Our model has a unit root with no structural change under the null hypothesis, while under the alternative it changes from a unit-root process to a stationary one or vice versa. We propose a Lagrange multiplier-type test, a test with the quasi-differencing method, and ,demeaned versions' of these tests. We find that the demeaned versions of these tests have better finite-sample properties, although they are not necessarily superior in asymptotics to the other tests. [source]

Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Nonignorable Missing Data

BIOMETRICS, Issue 3 2009
Hua Chen
Summary In this article, we first study parameter identifiability in randomized clinical trials with noncompliance and missing outcomes. We show that under certain conditions the parameters of interest are identifiable even under different types of completely nonignorable missing data: that is, the missing mechanism depends on the outcome. We then derive their maximum likelihood and moment estimators and evaluate their finite-sample properties in simulation studies in terms of bias, efficiency, and robustness. Our sensitivity analysis shows that the assumed nonignorable missing-data model has an important impact on the estimated complier average causal effect (CACE) parameter. Our new method provides some new and useful alternative nonignorable missing-data models over the existing latent ignorable model, which guarantees parameter identifiability, for estimating the CACE in a randomized clinical trial with noncompliance and missing data. [source]

Combining Multiple Biomarker Models in Logistic Regression

BIOMETRICS, Issue 2 2008
Zheng Yuan
Summary In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection. In this work, we propose a novel model-combining algorithm for classification in biomarker studies. It works by considering weighted combinations of various logistic regression models; five different weighting schemes are considered in the article. The weights and algorithm are justified using decision theory and risk-bound results. Simulation studies are performed to assess the finite-sample properties of the proposed model-combining method. It is illustrated with an application to data from an immunohistochemical study in prostate cancer. [source]