Narrow search
Showing 1 - 10 of 13,269
Cover Image
Identification and estimation using heteroscedasticity without instruments : the binary endogenous regressor
Lewbel, Arthur - 2016
Persistent link: https://www.econbiz.de/10011712374
Cover Image
Robust tests for white noise and cross-correlation
Dalla, Violetta; Giraitis, Liudas; Phillips, Peter C. B. - 2019
Persistent link: https://www.econbiz.de/10012062428
Cover Image
A test for kronecker product structure covariance matrix
Guggenberger, Patrik; Kleibergen, Frank; Mavroeidis, … - 2022
Persistent link: https://www.econbiz.de/10012814351
Cover Image
Robust tests for white noise and cross-correlation
Dalla, Violetta; Giraitis, Liudas; Phillips, Peter C. B. - 2020
Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation … heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series … spurious evidence of serial correlation. …
Persistent link: https://www.econbiz.de/10012243279
Cover Image
Robust inference on correlation under general heterogeneity
Giraitis, Liudas; Li, Yufei; Phillips, Peter C. B. - 2022
Persistent link: https://www.econbiz.de/10014235297
Cover Image
Nonlinear cointegrating power function regression with endogeneity
Hu, Zhishui; Phillips, Peter C. B.; Wang, Qiying - 2019
Persistent link: https://www.econbiz.de/10012132062
Cover Image
Advice on using heteroscedasticity based identification
Baum, Christopher F.; Lewbel, Arthur - 2019
Persistent link: https://www.econbiz.de/10012028715
Cover Image
Inference in linear regression models with many covariates and heteroskedasticity
Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K. - 2017
The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and...
Persistent link: https://www.econbiz.de/10011586174
Cover Image
Bayesian and maximin optimal designs for heteroscedastic regression models
Dette, Holger; Haines, Linda M.; Imhof, Lorens - 2003
The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs...
Persistent link: https://www.econbiz.de/10010511729
Cover Image
Treatment effects with many covariates and heteroskedasticity
Cattaneo, Matias D.; Jansson, Michael; Newey, Whitney K. - 2015
The linear regression model is widely used in empirical work in Economics. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results are...
Persistent link: https://www.econbiz.de/10011295589