Testing for Unit Roots with Income Distribution Data.

In this paper I test the unit root hypothesis for U.S. log GNP using the information available in income distribution data. The percentile data of an income distribution are shown to follow the same autoregressive pattern as does mean income. Under the null hypothesis of a unit root log GNP is cointegrated with the percentile data. A sequence of augmented HEGY-Tests, however, presents strong evidence against the unit root hypothesis for the distribution data and hence for log GNP. Using a full information estimation procedure for the percentiles under the alternative yields an estimate of the autoregressive coefficient which is in principle testable by an approximate Dickey-Hasza-Fuller test. The appropriate critical values are found by bootstrap methods. Again, inference is clearly unfavorable for the unit root hypothesis.