Add to ORKGIn this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. We show that while such an HAC estimator is not consistent, asymptotically valid testing is still possible. We show that tests using the Bartlett kernel without truncation are exactly equivalent to recent HAC robust tests proposed by Kiefer
This paper develops robust testing procedures for nonparametric kernel methods in the presence of te...
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
This paper considers studentized tests in time series regressions with nonparametrically autocorrela...
In this note we show that the heteroskedasticity-autocorrelation (HAC) robust tests recently propose...
Sharp origin kernels, constructed by taking powers of the Bartlett kernel, are suggested for use in...
A new \u85rst order asymptotic theory for heteroskedasticity-autocorrelation (HAC) robust tests base...
Employing power kernels suggested in earlier work by the authors (2003), this paper shows how to refi...
In this article, we consider time series OLS and IV regressions and introduce a new pair of commands...
This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. ...
In time series regressions with nonparametrically autocorrelated errors, it is now standard empirica...
04-142254. Jin acknowledges \u85nancial support from the NSFC (Grant No. 70601001). In time series r...
This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. ...
Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptot...
A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regres...
This paper considers a new class of heteroskedasticity and autocorrelation consistent (HAC) covarian...
This paper develops robust testing procedures for nonparametric kernel methods in the presence of te...
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
This paper considers studentized tests in time series regressions with nonparametrically autocorrela...
In this note we show that the heteroskedasticity-autocorrelation (HAC) robust tests recently propose...
Sharp origin kernels, constructed by taking powers of the Bartlett kernel, are suggested for use in...
A new \u85rst order asymptotic theory for heteroskedasticity-autocorrelation (HAC) robust tests base...
Employing power kernels suggested in earlier work by the authors (2003), this paper shows how to refi...
In this article, we consider time series OLS and IV regressions and introduce a new pair of commands...
This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. ...
In time series regressions with nonparametrically autocorrelated errors, it is now standard empirica...
04-142254. Jin acknowledges \u85nancial support from the NSFC (Grant No. 70601001). In time series r...
This paper proposes a new approach to testing in the generalized method of moments (GMM) framework. ...
Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptot...
A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regres...
This paper considers a new class of heteroskedasticity and autocorrelation consistent (HAC) covarian...
This paper develops robust testing procedures for nonparametric kernel methods in the presence of te...
A regression estimator is said to be robust if it is still reliable in the presence of outliers. On ...
This paper considers studentized tests in time series regressions with nonparametrically autocorrela...