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2 edition of Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices found in the catalog.

Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices

Robert M. de Jong

Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices

by Robert M. de Jong

  • 203 Want to read
  • 6 Currently reading

Published by Cardiff Business School in Cardiff .
Written in English


Edition Notes

Title from cover.

StatementRobert M. de Jong, James Davidson.
ContributionsDavidson, James., Cardiff Business School.
ID Numbers
Open LibraryOL21717473M

Details. The function meatHC is the real work horse for estimating the meat of HC sandwich estimators – the default vcovHC method is a wrapper calling sandwich and Zeileis () for more implementation details. The theoretical background, exemplified for the linear regression model, is described below and in Zeileis (). Since the variance–covariance matrix estimators are usually used for statistical inferences, it is important to study the performance of each estimator in terms of statistical inference. In our experiment we used the estimators to estimate the confidence interval coverage probabilities for the regression by:

This direction combines the power of both kernel methods and Riemannian geometry and represents a promising avenue for future research, both methodologically and practically. Symmetric Positive Definite (SPD) matrices, in particular covariance matrices, play an important role in many areas of mathematics, science, and engineering. Search this site: Humanities. Architecture and Environmental Design; Art History.

Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties James G. MacKinnon Department of Economics Queen's University Kingston, Ontario, Canada K7L 3N6 Halbert White Department of Economics University of California, San Diego La Jolla, CA U.S.A. Abstract. In statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. Estimation of covariance matrices then deals with the question of how to approximate the actual covariance matrix on the basis of a sample from the multivariate cases, where observations are complete, can be dealt with by using the .


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Consistency of kernel estimators of heteroscedastic and autocorrelated covariance matrices by Robert M. de Jong Download PDF EPUB FB2

Request PDF | Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices | Conditions are derived for the consistency of kernel estimators of the covari-ance matrix. Conditions are derived for the consistency of kernel estimators of the covariance matrix of a sum of vectors of dependent heterogeneous random variables, which match those of the currently best-known conditions for the central limit theorem, as required for a unified theory of asymptotic inference.

These include finite moments of order no more than 2 + for > 0, trending variances. Robert M. De Jong & James Davidson, "Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices," Econometrica, Econometric Society, vol.

68(2), pagesMarch. De Jong, R.M. and J. Da vidson,Consistency of k ernel estimators of heteroscedastic and auto correlated cov ariance matrices, mimeo, Mic higan State Univ ersit y.

Econometrica, Vol. 59, No. 3 (May, ), HETEROSKEDASTICITY AND AUTOCORRELATION CONSISTENT COVARIANCE MATRIX ESTIMATION BY DONALD W.

ANDREWS1 This paper is concerned with the estimation of covariance matrices in File Size: KB. One version of this is to use covariance matrices as the multivariate measure of dispersion.

Several authors have considered tests in this context, for both regression and grouped-data situations. [22] [23] Bartlett's test for heteroscedasticity between grouped data, used most commonly in the univariate case, has also been extended for the. The heteroscedasticity-consistent covariance matrix estimator (HCCME), also known as the sandwich (or robust or empirical) covariance matrix estimator, has been popular in recent years because it gives the consistent estimation of the covariance matrix of the parameter estimates even when the heteroscedasticity structure might be unknown or misspecified.

Heteroskedasticity Consistent Covariance Matrix Estimators for the 2 GMME of Spatial Autoregressive Models S uleyman Ta˘sp nar y Osman Doganz 4 J Abstract 6 In the presence of heteroskedasticity, the conventional test statistics, based on the ordinary least square estimator, lead to incorrect inference results in the linear regression model.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper considers strong consistency of heteroscedasticity and autocorrelation consistent covariance matrix estimators.

Sometimes such estimators in the literature are referred to as Newey-West estimators. Weak consistency proofs for these estimators can be found in White ().

covariance matrices. Section 3 reviews methods of estimating sparse precision matrices. Section 4 discusses robust covariance and precision matrix estimations using rank-based estimators.

Sections 5 and 6 respectively presents factor models based method, respectively in the cases of observable and unobservable factors. Section 7 introduces the Cited by: 3.

Inspection of the asymptotic normality results for least mean distance and generalized method of moments estimators given in, e.g., Theorems (a) and (a) shows that in both cases a matrix of the form \(C_n^{ - 1}{D_n}{D'_n}C_n^{ - 1'}\) acts as an asymptotic variance covariance matrix of \({n^{1/2}}\left({{{\hat \beta }_n} - {{\hat \beta }_n}} \right)\), where C n and D n are Author: Benedikt M.

Pötscher, Ingmar R. Prucha. autocorrelated. When the negative autocorrelation is sufficiently strong, some earlier estimators have a tendency to reject too infrequently, rejecting at the 5 percent level, for example, in distinctly less than 5 percent of the simulations. This complements the Andrews and Monahan () and Newey and West.

HAC estimators formed using the truncated kernel might not be positive semidefinite in finite samples. proposes using the Bartlett kernel as a remedy, but the resulting estimator is suboptimal in terms of its rate of consistency. The quadratic spectral kernel achieves an optimal rate of.

Three classes of estimators are considered: HAC - heteroskedasticity and autocorrelation consistent (Andrews, ; Newey and West, ) HC - hetheroskedasticity (White, ) CRVE - cluster robust (Arellano, ) The typical application of these estimators is to conduct robust inference about parameters of a model.

Adaptive Estimation of Heteroscedastic Linear Regression Models Using Heteroscedasticity Consistent Covariance Matrix Muhammad Aslam1 and Gulam Rasool Pasha2 Abstract For the estimation of linear regression models in the presence of heteroscedasticity of unknown form, method of ordinary least squares does not.

CONSISTENT COVARIANCE MATRIX ESTIMATION FOR LINEAR PROCESSES MIICCCHHHAAAEEELL JAANNNSSSSSSOOONN University of California, Berkeley Consistency of kernel estimators of the long-run covariance matrix of a linear process is established under weak moment and memory conditions+ In addition, it is pointed out that some existing.

Heteroscedastic Gaussian Process Regression 2. The Model Assume that we have a conditionally normal random variable, that is, y|x ∼N(µ(x),Σ(x)). In this case, p(y|x) is a member of the exponential family for appro-priate sufficient statistics Φ(x,y).

It is well known that in such cases the negative log-likelihood −logp(y|x;θ)File Size: KB. Consistent Covariance Matrix Estimation In Probit Models with Autocorrelated Errors 1.

Introduction Empirical macroeconomic applications of probit models (or more generally, models of variables with {0,1} outcomes) with time series data often. Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices, Econometrica, 68, – On Long-Run Covariance Matrix Estimation with the Truncated Flat Kernel.

In: Chen X., Swanson N. (eds) Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis. Cited by: 2. Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.

Donald Andrews () Econometrica,vol. 59, issue 3, Abstract: This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms.

Currently available estimators that are designed Cited by:. This paper studies spatial heteroskedasticity and autocorrelation consistent (HAC) estima-tion of covariance matrices of parameter estimators.

As heteroskedasticity is a well known feature of cross sectional data (e.g. White ()), spatial dependence is also a common property due to interactions among economic agents.ROBUST HCCM ESTIMATORS 6 which determine how much the ith squared residual should be inflated, given by the ratio between h max (maximal leverage) and h (mean leverage value of the h i), and k is a constant 0 Author: M.

Habshah, Muhammad Sani, Jayanthi Arasan.Doubly robust and efficient estimators for heteroscedastic partially linear single-index models allowing high dimensional covariates Yanyuan Ma Texas A&M University, College Station, USA and Liping Zhu Shanghai University of Finance and Economics, People’s Republic of China [Received November Final revision May ] Size: KB.