Testing for stationarity with covariates: more powerful tests with non-normal errors

Author:

Nazlioglu Saban1ORCID,Lee Junsoo2,Karul Cagin3,You Yu4

Affiliation:

1. Department of International Trade & Finance, Pamukkale University , Denizli , Turkey

2. Department of Economics and Finance , Nisantasi University , Istanbul , Turkey

3. Department of Econometrics , Pamukkale University , Denizli , Turkey

4. Advanced Institute of Finance and Economics , Liaoning University , Shenyang , Liaoning , China

Abstract

Abstract Previous 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.

Publisher

Walter de Gruyter GmbH

Subject

Economics and Econometrics,Social Sciences (miscellaneous),Analysis,Economics and Econometrics,Social Sciences (miscellaneous),Analysis

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