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Small-sample Properties (small-sample + property)
Selected AbstractsBootstrap prediction intervals for autoregressive models of unknown or infinite lag orderJOURNAL OF FORECASTING, Issue 4 2002Jae H. Kim Abstract Recent studies on bootstrap prediction intervals for autoregressive (AR) model provide simulation findings when the lag order is known. In practical applications, however, the AR lag order is unknown or can even be infinite. This paper is concerned with prediction intervals for AR models of unknown or infinite lag order. Akaike's information criterion is used to estimate (approximate) the unknown (infinite) AR lag order. Small-sample properties of bootstrap and asymptotic prediction intervals are compared under both normal and non-normal innovations. Bootstrap prediction intervals are constructed based on the percentile and percentile- t methods, using the standard bootstrap as well as the bootstrap-after-bootstrap. It is found that bootstrap-after-bootstrap prediction intervals show small-sample properties substantially better than other alternatives, especially when the sample size is small and the model has a unit root or near-unit root. Copyright © 2002 John Wiley & Sons, Ltd. [source] Methods for Generating Longitudinally Correlated Binary DataINTERNATIONAL STATISTICAL REVIEW, Issue 1 2008Patrick J. Farrell Summary The analysis of longitudinally correlated binary data has attracted considerable attention of late. Since the estimation of parameters in models for such data is based on asymptotic theory, it is necessary to investigate the small-sample properties of estimators by simulation. In this paper, we review the mechanisms that have been proposed for generating longitudinally correlated binary data. We compare and contrast these models with regard to various features, including computational efficiency, flexibility and the range restrictions that they impose on the longitudinal association parameters. Some extensions to the data generation mechanism originally suggested by Kanter (1975) are proposed. Résumé L'analyse des données longitudinales corrélées fait récemment l'objet d'un grand intérêt. Comme l'estimation des paramètres des modèles pour de telles données est souvent basée sur des études asymptotiques, il est nécessaire de procéder à des simulations pour explorer les propriétés des estimateurs en petits échantillonages. Dans ce papier, nous présentons une revue des méthodes qui ont été proposées pour générer des données binaires longitudinales corrélées. Nous les comparons sous différents aspects, notamment en termes d'efficience, flexibilité, et des restrictions qu'elles peuvent avoir sur les paramètres dits d'association longitudinale. Quelques extensions, de la méthode suggérée par Kanter (1975) pour générer de telles données, sont aussi proposées. [source] Bootstrap prediction intervals for autoregressive models of unknown or infinite lag orderJOURNAL OF FORECASTING, Issue 4 2002Jae H. Kim Abstract Recent studies on bootstrap prediction intervals for autoregressive (AR) model provide simulation findings when the lag order is known. In practical applications, however, the AR lag order is unknown or can even be infinite. This paper is concerned with prediction intervals for AR models of unknown or infinite lag order. Akaike's information criterion is used to estimate (approximate) the unknown (infinite) AR lag order. Small-sample properties of bootstrap and asymptotic prediction intervals are compared under both normal and non-normal innovations. Bootstrap prediction intervals are constructed based on the percentile and percentile- t methods, using the standard bootstrap as well as the bootstrap-after-bootstrap. It is found that bootstrap-after-bootstrap prediction intervals show small-sample properties substantially better than other alternatives, especially when the sample size is small and the model has a unit root or near-unit root. Copyright © 2002 John Wiley & Sons, Ltd. [source] New Improved Tests for Cointegration with Structural BreaksJOURNAL OF TIME SERIES ANALYSIS, Issue 2 2007Joakim Westerlund C12; C32; C33 Abstract., This article proposes Lagrange multiplier-based tests for the null hypothesis of no cointegration. The tests are general enough to allow for heteroskedastic and serially correlated errors, deterministic trends, and a structural break of unknown timing in both the intercept and slope. The limiting distributions of the test statistics are derived, and are found to be invariant not only with respect to the trend and structural break, but also with respect to the regressors. A small Monte Carlo study is also conducted to investigate the small-sample properties of the tests. The results reveal that the tests have small size distortions and good power relative to other tests. [source] Testing for Error Correction in Panel Data,OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 6 2007Joakim Westerlund Abstract This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for. [source] Consistent estimation of binary-choice panel data models with heterogeneous linear trendsTHE ECONOMETRICS JOURNAL, Issue 2 2006Alban Thomas Summary, This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and compare their statistical properties with standard fixed effects procedures. An empirical application to land renting decisions of Russian households between 1996 and 2002 is proposed. [source] Inference in Spline-Based Models for Multiple Time-to-Event Data, with Applications to a Breast Cancer Prevention TrialBIOMETRICS, Issue 4 2003Kiros Berhane Summary. As part of the National Surgical Adjuvant Breast and Bowel Project, a controlled clinical trial known as the Breast Cancer Prevention Trial (BCPT) was conducted to assess the effectiveness of tamoxifen as a preventive agent for breast cancer. In addition to the incidence of breast cancer, data were collected on several other, possibly adverse, outcomes, such as invasive endometrial cancer, ischemic heart disease, transient ischemic attack, deep vein thrombosis and/or pulmonary embolism. In this article, we present results from an illustrative analysis of the BCPT data, based on a new modeling technique, to assess the effectiveness of the drug tamoxifen as a preventive agent for breast cancer. We extended the flexible model of Gray (1994, Spline-based test in survival analysis, Biometrics50, 640,652) to allow inference on multiple time-to-event outcomes in the style of the marginal modeling setup of Wei, Lin, and Weissfeld (1989, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, Journal of the American Statistical Association84, 1065,1073). This proposed model makes inference possible for multiple time-to-event data while allowing for greater flexibility in modeling the effects of prognostic factors with nonlinear exposure-response relationships. Results from simulation studies on the small-sample properties of the asymptotic tests will also be presented. [source] | |||||||||||||