You are here

Covariate-augmented unit root tests with mixed-frequency data

Authors 
Publication Year 
2015
JEL Code 
C12 - Hypothesis Testing
C15 - Statistical Simulation Methods; Monte Carlo Methods
C22 - Time-Series Models
Abstract 
Unit root tests typically suffer from low power in small samples, which results in not rejecting the null hypothesis as often as they should. This paper tries to tackle this issue by assessing whether it is possible to improve the power performance of covariate-augmented unit root tests, namely the ADF family of tests, by exploiting mixed-frequency data. We use the mixed data sampling (MIDAS) approach to deal with mixed-frequency data. The results from a Monte Carlo exercise indicate that mixed-frequency tests have better power performance than low-frequency tests. The gains from exploiting mixed-frequency data are greater for near-integrated variables. An empirical illustration using the US unemployment rate is presented.
Document link 
Published as 
Tags