Results 1  10
of
5,521
Large Sample Sieve Estimation of SemiNonparametric Models
 Handbook of Econometrics
, 2007
"... Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; seminonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method o ..."
Abstract

Cited by 185 (19 self)
 Add to MetaCart
extremum estimates, convergence rates of the sieve Mestimates, pointwise normality of series estimates of regression functions, rootn asymptotic normality and efficiency of sieve estimates of smooth functionals of infinite dimensional parameters. Examples are used to illustrate the general results.
Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests, With an Application to the PPP Hypothesis; New Results. Working paper
, 1997
"... We examine properties of residualbased tests for the null of no cointegration for dynamic panels in which both the shortrun dynamics and the longrun slope coefficients are permitted to be heterogeneous across individual members of the panel+ The tests also allow for individual heterogeneous fixed ..."
Abstract

Cited by 529 (13 self)
 Add to MetaCart
fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests+ We derive limiting distributions for these and show that they are normal and free of nuisance parameters+ We also provide Monte Carlo evidence to demonstrate their small sample size
Accurate Methods for the Statistics of Surprise and Coincidence
 COMPUTATIONAL LINGUISTICS
, 1993
"... Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used unjustifiably ..."
Abstract

Cited by 1057 (1 self)
 Add to MetaCart
Much work has been done on the statistical analysis of text. In some cases reported in the literature, inappropriate statistical methods have been used, and statistical significance of results have not been addressed. In particular, asymptotic normality assumptions have often been used
Testing for Common Trends
 Journal of the American Statistical Association
, 1988
"... Cointegrated multiple time series share at least one common trend. Two tests are developed for the number of common stochastic trends (i.e., for the order of cointegration) in a multiple time series with and without drift. Both tests involve the roots of the ordinary least squares coefficient matrix ..."
Abstract

Cited by 464 (7 self)
 Add to MetaCart
has k unit roots and n k distinct stationary linear combinations. Our proposed tests can be viewed alternatively as tests of the number of common trends, linearly independent cointegrating vectors, or autoregressive unit roots of the vector process. Both of the proposed tests are asymptotically
Equivariant Adaptive Source Separation
 IEEE Trans. on Signal Processing
, 1996
"... Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Eq ..."
Abstract

Cited by 449 (9 self)
 Add to MetaCart
algorithm does not depend on the mixing matrix. In particular, convergence rates, stability conditions and interference rejection levels depend only on the (normalized) distributions of the source signals. Close form expressions of these quantities are given via an asymptotic performance analysis
Seminonparametric IV Estimation of ShapeInvariant Engel Curves
 Econometrica
, 2007
"... This paper studies a shapeinvariant Engel curve system with endogenous total expenditure, in which the shapeinvariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both t ..."
Abstract

Cited by 78 (17 self)
 Add to MetaCart
for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Rootn asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of “low
Nonparametric Identification and Estimation of Nonclassical ErrorsinVariables Models Without Additional Information
"... This paper considers identification and estimation of a nonparametric regression model with an unobserved discrete covariate. The sample consists of a dependent variable and a set of covariates, one of which is discrete and arbitrarily correlates with the unobserved covariate. The observed discrete ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
and a rank condition that is directly testable given the data. Our identification strategy does not require additional sample information, such as instrumental variables or a secondary sample. We then estimate the model via the method of sieve maximum likelihood, and provide rootn asymptotic normality
Kernel Estimation of Average Derivatives and Differences
, 2003
"... In this paper, we consider the problem of estimating average derivatives and differences using kernel estimators. We derive a new nonparametric estimator which we refer to as average difference estimator. We show that this estimator is consistent and rootN asymptotically normally distributed. Furth ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
In this paper, we consider the problem of estimating average derivatives and differences using kernel estimators. We derive a new nonparametric estimator which we refer to as average difference estimator. We show that this estimator is consistent and rootN asymptotically normally distributed
Average Difference Estimation of Nonlinear Pricing Models
, 2000
"... In this paper, we derive a nonparametric test for nonlinear pricing. Our application focuses on labor markets in which nonlinear pricing arises naturally in a contractual framework. To implement our testing procedure we build on the recent literature on average derivative estimators. Our empirical r ..."
Abstract
 Add to MetaCart
develop a new estimator, the average difference estimator. We show that this estimator is consistent and rootN asymptotically normally distributed. Furthermore, the average difference estimator converges to the average derivative estimator as the increment used to compute the difference converges to zero
RootN Consistent Estimation In A Random Coefficient Autoregressive Model
"... . This paper deals with rootn consistent estimation of the parameter ¯ in the RCAR(1) model defined by the difference equation X j = (¯ + U j )X j \Gamma1 + " j ; j 2 Z; where f" j : j 2 Zg and fU j : j 2 Zg are two independent sets of i.i.d. random variables with zero means, positive fin ..."
Abstract
 Add to MetaCart
. This paper deals with rootn consistent estimation of the parameter ¯ in the RCAR(1) model defined by the difference equation X j = (¯ + U j )X j \Gamma1 + " j ; j 2 Z; where f" j : j 2 Zg and fU j : j 2 Zg are two independent sets of i.i.d. random variables with zero means, positive
Results 1  10
of
5,521