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Robust block bootstrap panel predictability tests

Westerlund, Joakim; Smeekes, Stephan - 2013

Persistent link: https://www.econbiz.de/10010199463

Pitfalls when estimating treatment effects using clustered data

MacKinnon, James G.; Webb, Matthew - 2017

Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard...

Persistent link: https://www.econbiz.de/10011722291

Tests of no cross-sectional error dependence in panel quantile regressions

Demetrescu, Matei; Hosseinkouchack, Mehdi; Rodrigues, … - 2023

This paper argues that cross-sectional dependence (CSD) is an indicator of misspecification in panel quantile … regression (QR) rather than just a nuisance that may be accounted for with panel-robust standard errors. This motivates the … development of a novel test for panel QR misspecification based on detecting CSD. The test possesses a standard normal limiting …

Persistent link: https://www.econbiz.de/10014366629

Testing for a general class of functional inequalities

Lee, Sokbae; Song, Kyungchul; Whang, Yoon-jae - 2014

In this paper, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a uni ed framework, with a number of possible applications in auction models, game theoretic models, wage inequality, and...

Persistent link: https://www.econbiz.de/10010254852

Cluster-robust jackknife and bootstrap inference for binary response models

MacKinnon, James G.; Nielsen, Morten Ørregaard; Webb, … - 2024

We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves...

Persistent link: https://www.econbiz.de/10015048740

How to implement the bootstrap in static or stable dynamic regression models

Giersbergen, Noud P. A. van; Kiviet, J. F. - 2001

By combining two alternative formulations of a test statistic with two alternative resamplingschemes we obtain four different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and power problems, whereas the remaining two areadequate...

Persistent link: https://www.econbiz.de/10011325661

When and how to deal with clustered errors in regression models

MacKinnon, James G.; Webb, Matthew - 2019

We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error terms that are arbitrarily correlated within known clusters but uncorrelated across them. The...

Persistent link: https://www.econbiz.de/10012183510

A robust permutation test for subvector inference in linear regressions

D'Haultfœuille, Xavier; Tuvaandorj, Purevdorj - In: Quantitative economics : QE ; journal of the … 15 (2024) 1, pp. 27-87

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then we show that the test is asymptotically of correct level, consistent, and has power against local alternatives when the...

Persistent link: https://www.econbiz.de/10014496927

HAR Testing for Spurious Regression in Trend

Phillips, Peter C. B. - 2018

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these signiﬁcance tests...

Persistent link: https://www.econbiz.de/10012906697

Har testing for spurious regression in trend

Phillips, Peter C. B.; Wang, XiaoHu; Zhang, Yonghui - In: Econometrics : open access journal 7 (2019) 4/50, pp. 1-28

Persistent link: https://www.econbiz.de/10012160687