Home  About us  Contact
 

Consistent Estimation (consistent + estimation)

Distribution by Scientific Domains
Mathematics and Statistics44%
Business, Economics, Finance and Accounting44%

Selected Abstracts

Consistent estimation of binary-choice panel data models with heterogeneous linear trends

THE ECONOMETRICS JOURNAL, Issue 2 2006
Alban 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]

ACCELERATED FAILURE TIME MODELS WITH NONLINEAR COVARIATES EFFECTS

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2007
Chenlei Leng
Summary As a flexible alternative to the Cox model, the accelerated failure time (AFT) model assumes that the event time of interest depends on the covariates through a regression function. The AFT model with non-parametric covariate effects is investigated, when variable selection is desired along with estimation. Formulated in the framework of the smoothing spline analysis of variance model, the proposed method based on the Stute estimate (Stute, 1993[Consistent estimation under random censorship when covariables are present, J. Multivariate Anal.45, 89,103]) can achieve a sparse representation of the functional decomposition, by utilizing a reproducing kernel Hilbert norm penalty. Computational algorithms and theoretical properties of the proposed method are investigated. The finite sample size performance of the proposed approach is assessed via simulation studies. The primary biliary cirrhosis data is analyzed for demonstration. [source]

Estimation of Nonlinear Models with Measurement Error

ECONOMETRICA, Issue 1 2004
Susanne M. Schennach
This paper presents a solution to an important econometric problem, namely the root n consistent estimation of nonlinear models with measurement errors in the explanatory variables, when one repeated observation of each mismeasured regressor is available. While a root n consistent estimator has been derived for polynomial specifications (see Hausman, Ichimura, Newey, and Powell (1991)), such an estimator for general nonlinear specifications has so far not been available. Using the additional information provided by the repeated observation, the suggested estimator separates the measurement error from the "true" value of the regressors thanks to a useful property of the Fourier transform: The Fourier transform converts the integral equations that relate the distribution of the unobserved "true" variables to the observed variables measured with error into algebraic equations. The solution to these equations yields enough information to identify arbitrary moments of the "true," unobserved variables. The value of these moments can then be used to construct any estimator that can be written in terms of moments, including traditional linear and nonlinear least squares estimators, or general extremum estimators. The proposed estimator is shown to admit a representation in terms of an influence function, thus establishing its root n consistency and asymptotic normality. Monte Carlo evidence and an application to Engel curve estimation illustrate the usefulness of this new approach. [source]

Identification of the inertia matrix of a rotating body based on errors-in-variables models

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2010
Byung-Eul Jun
Abstract This paper proposes a procedure for identifying the inertia matrix of a rotating body. The procedure based on Euler's equation governing rotational motion assumes errors-in-variables models in which all measurements, torque as well as angular velocities, are corrupted by noises. In order for consistent estimation, we introduce an extended linear regression model by augmenting the regressors with constants and the parameters with noise-contributed terms. A transformation, based on low-pass filtering, of the extended model cancels out angular acceleration terms in the regressors. Applying the method of least correlation to the model identifies the elements of the inertia matrix. Analysis shows that the estimates converge to the true parameters as the number of samples increases to infinity. Monte Carlo simulations demonstrate the performance of the algorithm and support the analytical consistency. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Econometric evidence of cross-market effects of generic dairy advertising

AGRIBUSINESS : AN INTERNATIONAL JOURNAL, Issue 1 2010
Metin Cakir
We estimate a dairy demand system to evaluate generic dairy advertising in the US, 1990,2005. Previous empirical studies of generic dairy advertising focus only on the market of the advertised good, ignoring potential spill-over and feedback effects. We specify an LA/AIDS model of dairy demand, which allows consistent estimation of cross-price and cross-advertising effects across dairy product markets, and is flexible and satisfies the axioms of consumer theory. We use the non-linear 3SLS estimator to address endogenous prices and serial correlation, and conduct bootstrapping to generate empirical distributions of elasticity estimates. Results suggest that cross-market effects are economically and statistically important. Thus, econometric dairy demand models that ignore cross-advertising and cross-price effects are mis-specified. Previous work that ignores substitution between fluid milk and cheese overstates producers' returns to generic advertising for either product. © 2010 Wiley Periodicals, Inc. [source]

Estimating the order of a hidden markov model

THE CANADIAN JOURNAL OF STATISTICS, Issue 4 2002
Rachel J. Mackay
Abstract While the estimation of the parameters of a hidden Markov model has been studied extensively, the consistent estimation of the number of hidden states is still an unsolved problem. The AIC and BIC methods are used most commonly, but their use in this context has not been justified theoretically. The author shows that for many common models, the penalized minimum-distance method yields a consistent estimate of the number of hidden states in a stationary hidden Markov model. In addition to addressing the identifiability issues, she applies her method to a multiple sclerosis data set and assesses its performance via simulation. Bien que les travaux traitant de I'estimation des paramétres d'une chatne de Markov cachéd soient nombreux, le probléme d'estimer de facon convergente le nombre détats cachés reste ouvert. Les méthodes du CIA et du CIB sont souvent utilisées a cette fin, sans toutefois que leur emploi ait été justifié théoriquement. L'auteur montre ici que, sous des conditions convenables, la méthode de distance minimum pénalisée conduit à une estimation convergente du nombre d'états cachés dans une chaine de Markov cachée stationnaire. En plus d'aborder le probléme d'identifiabilité, elle applique sa méthode à des données concernant la sclérose en plaques et en évalue la performance a taille finie par voie de simulation. [source]

Distinguishing between trend-break models: method and empirical evidence

THE ECONOMETRICS JOURNAL, Issue 2 2001
Chih-Chiang Hsu
We demonstrate that in time trend models, the likelihood-based tests of partial parameter stability have size distortions and cannot be applied to detect the changing parameter. A two-step procedure is then proposed to distinguish between different trend-break models. This procedure involves consistent estimation of break dates and properly-sized tests for changing coefficient. In the empirical study of the Nelson-Plosser data set, we find that the estimated change points and trend-break specifications resulting from the proposed procedure are quite different from those of Perron (1989, 1997), Chu and White (1992), and Zivot and Andrews (1992). In another application, our procedure provides formal support for the conclusion of Ben-David and Papell (1995) that real per capita GDPs of most OECD countries exhibit a slope change in trend. [source]

Marginal Mark Regression Analysis of Recurrent Marked Point Process Data

BIOMETRICS, Issue 2 2009
Benjamin French
Summary Longitudinal studies typically collect information on the timing of key clinical events and on specific characteristics that describe those events. Random variables that measure qualitative or quantitative aspects associated with the occurrence of an event are known as marks. Recurrent marked point process data consist of possibly recurrent events, with the mark (and possibly exposure) measured if and only if an event occurs. Analysis choices depend on which aspect of the data is of primary scientific interest. First, factors that influence the occurrence or timing of the event may be characterized using recurrent event analysis methods. Second, if there is more than one event per subject, then the association between exposure and the mark may be quantified using repeated measures regression methods. We detail assumptions required of any time-dependent exposure process and the event time process to ensure that linear or generalized linear mixed models and generalized estimating equations provide valid estimates. We provide theoretical and empirical evidence that if these conditions are not satisfied, then an independence estimating equation should be used for consistent estimation of association. We conclude with the recommendation that analysts carefully explore both the exposure and event time processes prior to implementing a repeated measures analysis of recurrent marked point process data. [source]

Estimating Mean Response as a Function of Treatment Duration in an Observational Study, Where Duration May Be Informatively Censored

BIOMETRICS, Issue 2 2004
Brent A. Johnson
Summary. After a treatment is found to be effective in a clinical study, attention often focuses on the effect of treatment duration on outcome. Such an analysis facilitates recommendations on the most beneficial treatment duration. In many studies, the treatment duration, within certain limits, is left to the discretion of the investigators. It is often the case that treatment must be terminated prematurely due to an adverse event, in which case a recommended treatment duration is part of a policy that treats patients for a specified length of time or until a treatment-censoring event occurs, whichever comes first. Evaluating mean response for a particular treatment-duration policy from observational data is difficult due to censoring and the fact that it may not be reasonable to assume patients are prognostically similar across all treatment strategies. We propose an estimator for mean response as a function of treatment-duration policy under these conditions. The method uses potential outcomes and embodies assumptions that allow consistent estimation of the mean response. The estimator is evaluated through simulation studies and demonstrated by application to the ESPRIT infusion trial coordinated at Duke University Medical Center. [source]