C - Mathematical and Quantitative Methods
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A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data
This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false.