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Consistent Hac Estimation and Robust Regression Testing Using Sharp Origin Kernels with No Truncation

Phillips, Peter C. B.; Sun, Yixiao; Jin, Sainan - 2003

A new family of kernels is suggested for use in heteroskedasticity and autocorrelation consistent (HAC) and long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or...

Persistent link: https://www.econbiz.de/10014088395

Nonparametric Generalized Least Squares in Applied Regression Analysis

O'Hara, Michael E. - 2013

variants of heteroscedasticity robust standard error estimators (HRSEs) in an applied setting. Given myriad alternative HRSEs … various approaches to handling heteroscedasticity to data on professor rankings obtained from ratemyprofessor.com and find not … only heteroscedasticity to be prevalent but NPGLS and FGLS provide different insights and the statistical significance of …

Persistent link: https://www.econbiz.de/10013077989

Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks

Chernozhukov, Victor; Fernández‐Val, Iván - 2011

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many...

Persistent link: https://www.econbiz.de/10014178700

Application of the Scaling Functions to Nonparametric Regression

Manole, Daniel Sorin - 2007

For estimating regression function we can use many proceedings. In this paper, we have chosen to apply scaling functions to the estimation of regression functions. When one knows many bivariate date with the values of two variables, in the goal to express a correlation between the two variables...

Persistent link: https://www.econbiz.de/10014051848

Nonlinear Log-Periodogram Regression for Perturbed Fractional Processes

Sun, Yixiao; Phillips, Peter C. B. - 2003

This paper studies fractional processes that may be perturbed by weakly dependent time series. The model for a perturbed fractional process has a components framework in which there may be components of both long and short memory. All commonly used estimates of the long memory parameter (such as...

Persistent link: https://www.econbiz.de/10014116703

Inference on Quantile Regression Process, an Alternative

Chernozhukov, Victor - 2003

A wide variety of important distributional hypotheses can be assessed using the empirical quantile regression processes. In this paper, a very simple and practical resampling test is offered as an alternative to inference based on Khmaladzation, as developed in Koenker and Xiao (2002). This...

Persistent link: https://www.econbiz.de/10014119496

Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)

Billio, Monica - 2017

Seemingly unrelated regression (SUR) models are useful in studying the interactions among different variables. In a high dimensional setting or when applied to large panel of time series, these models require a large number of parameters to be estimated and suffer of inferential problems.To...

Persistent link: https://www.econbiz.de/10012968298

Instrumental Variable Quantile Regression

Chernozhukov, Victor; Hansen, Christian Bailey - 2006

The paper develops estimation and inference methods for econometric models with partial identification, focusing on models defined by moment inequalities and equalities. Main applications of this framework include analysis of game-theoretic models, regression with missing and mismeasured data,...

Persistent link: https://www.econbiz.de/10014026967

L1-Penalized Quantile Regression in High Dimensional Sparse Models

Chernozhukov, Victor - 2009

We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than the sample size n, but only s of these regressors have non-zero impact on the conditional quantile of...

Persistent link: https://www.econbiz.de/10013160364

Inference for extremal conditional quantile models, with an application to market and birthweight risks

Chernozhukov, Victor; Fernández-Val, Iván - 2009 - This version: December 26, 2009

Persistent link: https://www.econbiz.de/10009419329