Improving the Finite Sample Performance of Tests for a Shift in Mean

It is widely known that structural break tests based on the long-run variance estimator, which is estimated under the alternative, suffer from serious size distortion when the errors are serially correlated. In this paper, we propose bias-corrected tests for a shift in mean by correcting the bias of the long-run variance estimator up to O(1/T). Simulation results show that the proposed tests have good size and high power.

MoreLess

Year of publication:	2014-11-10
Authors:	YAMAZAKI, Daisuke ; KUROZUMI, Eiji
Institutions:	Graduate School of Economics, Hitotsubashi University
Subject:	structural change \| long-run variance \| bias correction

More details

Extent:	application/pdf
Series:	Discussion Papers.
Type of publication:	Book / Working Paper
Notes:	Number 2014-16 42 pages long [7] p.
Classification:	C12 - Hypothesis Testing ; C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011074870