Powerful Trend Function Tests That Are Robust to Strong Serial Correlation with an Application to the Prebisch-Singer Hypothesis

In this paper we propose tests for hypotheses regarding the parameters of the deterministic trend function of a univariate time series. The tests do not require knowledge of the form of serial correlation in the data and they are robust to strong serial correlation. The data can contain a unit root and the tests still have the correct size asymptotically. The tests we analyze are standard heteroskedasticity autocorrelation (HAC) robust tests based on nonparametric kernel variance estimators. We analyze these tests using the ï¾…xed-b asymptotic framework recently proposed by Kiefer and Vogelsang (2002). This analysis allows us to analyze the power properties of the tests with regards to bandwidth and kernel choices. Our analysis shows that among popular kernels, there are speciï¾…c kernel and bandwidth choices that deliver tests with maximal power within a speciï¾…c class of tests. Based on the theoretical results, we propose a data dependent bandwidth rule that maximizes integrated power. Our recommended test is shown to have power that dominates a related test proposed by Vogelsang (1998). We apply the recommended test to the logarithm of a net barter terms of trade series and we ï¾…nd that this series has a statistically signiï¾…cant negative slope. This ï¾…nding is consistent with the well known Prebisch-Singer hypothesis.