Functional-coefficient models under unit root behaviour
We analyze the statistical properties of non-parametrically estimated functions in a functional-coefficient model if the data have a unit root. We show that the estimated function converges at a faster rate than under the stationary case. However, the estimator has a mixed normal distribution so that point-wise confidence intervals are calculated using the usual normal distribution theory rather than a Dickey--Fuller distribution. The results are used to show how one can discriminate between a unit root process and a non-linear functional-coefficient process. We illustrate the procedure using U.S. unemployment and interest rate data. Copyright 2005 Royal Economic Society
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Volume (Year): 8 (2005)
Issue (Month): 2 (July)
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