| Summary: | 碩士 === 國立臺灣大學 === 經濟學研究所 === 97 === New data-driven smooth tests are proposed in this thesis. The new tests
are proposed to eschew the downward weighting problem of the traditional
omnibus tests, and the new tests are constructed based on the components
of Karhunen-Lo′eve expansion of limiting process. As examples, we construct
tests for the null hypothesis of stationarity, coefficient stability, symmetric
dynamics of quantile autoregressive model, and bivariate independence.
Simulation results show that, new tests have moderate size control and nice
power performance for a wide range of alternatives. In contrast to traditional
omnibus tests, new tests are more robust to complex models and perform well
under high-frequency alternatives.
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