Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
AbstractPrevious studies suggested that the power of unit root and stationarity tests can be improved by augmenting a testing regression model with stationary covariates. However, one practical problem arises since such procedures require finding the variables that satisfy certain conditions. The difficulty of finding satisfactory covariate has hindered using such desired tests. In this paper, we suggest using non-normal errors to construct stationary covariates in testing for stationarity. We do not need to look for outside variables, but we utilize the distributional information embodied in a time series of interest. The terms driven from the information on non-normal errors can be employed as valid stationary covariates. For this, we adopt the framework of stationarity tests of Jansson (Jansson, M. 2004. “Stationarity Testing with Covariates.” Econometric Theory 20: 56–94). We show that the tests can achieve much-improved power. We then present the response surface function estimates to facilitate computing the critical values and the corresponding p-values. We investigate the nature of shocks to the US macro-economic series using the updated Nelson–Plosser data set through our new testing procedure. We find stronger evidence of non-stationarity than their univariate counterparts that do not use the covariates.
Studies in Nonlinear Dynamics & Econometrics – de Gruyter
Published: Apr 21, 2022
Keywords: Nelson–Plosser; non-normality; RALS; stationarity; C12; C15; C22
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.