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Essays on the Efficiency of Tests in Econometrics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays on the Efficiency of Tests in Econometrics./
作者:	Engle, Samuel P.
面頁冊數:	1 online resource (99 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29390205click for full text (PQDT)
ISBN:	9798841769910

Essays on the Efficiency of Tests in Econometrics.
Engle, Samuel P.

Essays on the Efficiency of Tests in Econometrics. - 1 online resource (99 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2022.

Includes bibliographical references

The first chapter studies the implications of variance estimation for the efficiency of Wald tests. When constructing Wald tests, consistency is the key property required for the variance estimator. This property ensures asymptotic validity of Wald tests and confidence intervals. Classical efficiency comparisons of hypothesis tests indicate all consistent variance estimators lead to equivalent Wald tests. This chapter develops a simple relative efficiency measure which leads to several new conclusions. These include quantifying the power loss associated with using cluster-robust variance estimators when using overly coarse clusters, recommending particular kernels for estimating the asymptotic variance in quantile regression, and comparing the power of Anderson-Rubin tests to the standard Wald test. As a byproduct, the asymptotic distributions of several test statistics are derived under fixed alternatives. Simulation evidence indicates the new asymptotic efficiency measure provides good finite-sample predictions. In an application using data from the American Community Survey, it is demonstrated how to use the new approach for conducting power analysis when looking at the effect of minimum wage increases on employment. The second chapter studies the efficiency of the one-sample t-test when the observations come from a heavy-tailed distribution. The t-test is a standard inferential procedure in economics and finance. When the data exhibit heavy tails, the t-test may have low power. This chapter characterizes the rate at which power converges to 1 for data in a particular class of heavy tailed distributions, including some distributions with all finite moments. While classical results on the rate of convergence of power focus on exponential rates, we find the rate to be a much slower polynomial rate when the data have heavy tails. Motivated by the poor power of the t-test when the data exhibit heavy-tailed behavior, we propose an alternative testing procedure that matches the local asymptotic efficiency properties of the t-test while achieving at least semiexponential Type-II error rates when the tails are heavy. Our test construction utilizes sample splitting and robust recombination techniques developed in the machine learning literature to obtain a test statistic with thinner tails and better performance.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841769910Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Hypothesis testingIndex Terms--Genre/Form:

542853
Electronic books.

Essays on the Efficiency of Tests in Econometrics.
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