Testing for unit roots in economic time series with missing observations

This paper considers unit root testing of time-series data with missing obs ervations. Three procedures for dealing with the gaps are discussed. These include: ignoring the gaps, replacing the gaps with the last available obse rvation, and filling the gaps with a linear interpolation method. The tests for the first two procedures yield test statistics which have the same asy mptotic distribution as that tabulated by Dickey and Fuller (1979) for the complete data situation. The remaining procedure yields a test statistic th at has an asymptotic distribution that differs from Dickey and Fuller s tab ulated distribution by an adjustment factor. In addition, models that inclu de an ARIMA (0,1,q) error and augmented Dickey-Fuller tests are also consid ered in this paper. A simulation experiment is performed for the above mode ls using the A-B sampling scheme. The results show that ignoring gaps in ti me-series data with missing observations produces unit root tests that are more powerful than the other two approaches that are considered.