2 edition of **Convergence rates & asymptotic normality for series estimators** found in the catalog.

- 231 Want to read
- 19 Currently reading

Published
**1995**
by Dept. of Economics, Massachusetts Institute of Technology in Cambridge, Mass
.

Written in English

**Edition Notes**

Other titles | Convergence rates and asymptotic normality for series estimators. |

Statement | Whitney K. Newey |

Series | Working paper / Dept. of Economics -- no. 95-13, Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 95-13. |

The Physical Object | |
---|---|

Pagination | 30 p. : |

Number of Pages | 30 |

ID Numbers | |

Open Library | OL24637900M |

OCLC/WorldCa | 33353456 |

the bootstrap variance estimator is asymptotically normal with a convergence rate of order O(n 5=4). Given that the quantile variance decays at the rate of O(n 1), the relative standard deviation of a bootstrap estimator is O(n 1=4). The technical challenge lies in that many classic results of order statistics are not applicable. A note on "Convergence rates and asymptotic normality for series estimators": uniform convergence rates.

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the. The rate of convergence is root of n when |τ| estimator (MLE), but when τ = 1 the rate of convergence to the normal distribution is within a slowly varying factor of n.

Linton and Jacho-Chávez [] obtained some asymptotic normality results of the internal estimator \(\widetilde{m}_{n}(x)\) under independent ing Theorem 1 and Corollary 1 of Linton and Jacho-Chávez [], our asymptotic normality results on the internal estimator \(\widehat{m}_{n}(x)\) in Theorems and are relatively ile, we use the method of . Asymptotic normality of the latter estimator was obtained, in the case of - mixing, by Quintela-del-Rio (). We refer to Ferraty et al. () and Mahhiddine et al. () for uniform almost complete convergence of the functional component of this nonparametric model. When hazard rate estimation is performed with multiple variables, the re-.

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Nevertheless, these uniform convergence rates improve on some in the literature, e.g. on Cox (). Also, it does not yet seem to be known whether it is possible to attain the optimal uniform convergence rates using a series estimator.

Asymptotic normality There are many applications where a functional of a conditional expectation is of Cited by: Both mean-square and uniform convergence rates are derived. Asymptotic normality is shown for nonlinear functionals of series estimators, covering many cases not previously treated.

Also, a simple condition for n-consitency of a functional of a series estimator is given. The regularity conditions are straightforward to understand, and several Cited by: We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e.

sup-norm) convergence rate (n / log n) − p / (2 p + d) of Stone (), where d is the number of regressors and p is the smoothness of the regression function.

The optimal rate is achieved even for heavy-tailed martingale difference errors with finite (2 + (d / p)) th Cited by: Newey, Whitney K., "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol. 79(1), pages Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound is shown for one of the three estimators.

In the final part of the paper, an empirical application to a job training program reveals the importance of heterogeneous treatment effects, showing that for this program the effects are concentrated in.

A note on “Convergence rates and asymptotic normality for series estimators”: uniform convergence rates Robert M. de Jong⁄ Michigan State University This revision, January Abstract This paper establishes improved uniform convergence rates for series estimators.

Series estimators are least-squares ﬁts of a regression function where. This paper establishes improved uniform convergence rates for series estimators.

Series estimators are least-squares fits of a regression function where the number of regressors depends on sample size. I will specialize my results to the cases of polynomials and regression by: Optimal Uniform Convergence Rates and Asymptotic Normality for Series Estimators Under Weak Dependence and Weak Conditions Xiaohong Chenyand Timothy M.

Christensenz First version January ; Revised AugustJuly Abstract We show that spline and wavelet series regression estimators for weakly dependent regressors attain.

These rates are valid for i.i.d. data as well as for uniform mixing and absolutely regular (/spl beta/-mixing) stationary time series data. In addition, the rates are fast enough to deliver root-n asymptotic normality for plug-in estimates of smooth functionals using general ANN sieve estimators.

accuracyoftheestimators(,).Also,theyareusefulforthe theoryofsemiparametricestimatorsthatdependonprojectionestimates( a. A note on "Convergence rates and asymptotic normality for series estimators": Uniform convergence rates Article in Journal of Econometrics (1) February with 56 Reads.

Andrews, D.W.K. () Asymptotic normality of series estimators for nonparametric and semiparametric models. () A note on “Convergence rates and asymptotic normality for series estimators”: Uniform convergence rates.

Journal of Econometrics1. This paper gives general conditions for convergence rates and asymptotic normality of series estimators of conditional expectations, and specializes these conditions to polynomial regression and. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper establishes improved uniform convergence rates for series estimators.

Series estimators are least-squares fits of a regression function where the number of regressors depends on sample size. I will specialize my results to the cases of polynomials and regression splines. We show that spline and wavelet series regression estimators for weakly dependent regressors attain the optimal uniform (i.e., sup-norm) convergence rate (n /log n) - p /(2p+ d) of Stone (), where d is the number of regressors and p is the smoothness of the regression function.

The optimal rate is achieved even for heavy-tailed martingale diﬀerence errors with ﬁnite (2 + (d/p))th. PDF | On Feb 1,Whitney K.

Newey published Convergence Rates & Asymptotic Normality Estimators | Find, read and cite all the research you need on ResearchGate.

We obtain the rate of uniformly asymptotic normality of the weighted estimator which is nearly O (n − 1 ∕ 4) when the moment condition is appropriate. The results generalize the corresponding ones of Yang () from NA samples to LNQD samples and improve or extend the corresponding one of Li et al.

() for LNQD samples. Newey, Whitney K., "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol.

79(1), pagesJuly. Full references (including those not matched with items on IDEAS). Under general conditions, the asymptotic normality for the wavelet estimators and the convergence rates for the wavelet estimators of nonparametric components are investigated.

A numerical example. Almost sure convergence, convergence in probability and asymptotic normality In the previous chapter we considered estimator of several diﬀerent parameters. The hope is that as the sample size increases the estimator should get ‘closer’ to the parameter of interest.

When we. The asymptotic normality of the DHD estimators of (Formula presented.) and (Formula presented.) are investigated, and the weak convergence rate of the estimator of (Formula presented.) is also."A note on "Convergence rates and asymptotic normality for series estimators": uniform convergence rates," Journal of Econometrics, Elsevier, vol.

(1), pagesNovember. Stoker, Thomas M, " Consistent Estimation of Scaled Coefficients," Econometrica, Econometric Society, vol. 54(6), pagesNovember.Rates of convergence in the asymptotic normality for some local maximum estimators V. Paulauskas Lithuanian Mathematical Journal vol pages 68 – 91 () Cite this article.