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The article proposes a novel principal volatil- ity component (PVC) technique based on a generalized kurto- sis matrix in a time series context. The proposed test statistics allow
of simulations that illustrate the insensitivity of multiple im- putation inferences to specifications of the prior distributions for the CDPMMN imputation engine, (ii) the
(2007), “Long Run Variance Estimation and Robust Regression Testing Using Sharp Origin Kernels With No Trun- cation,” Journal of Statistical Planning and Inference , 137,
Figure 5 shows the empirical cumulative distribution function of the PIT of the autoregressive model forecasts (dark, solid line) together with the uniform cumulative
We conduct a comprehensive empirical analysis of continuous-time models for equity index returns with the aim of investigating the properties of several widely used model
We present the asymptotic properties of the proposed estimator in Section 3 and present the finite sample performance of our estimator via some numerical simulations both in the iid
Our procedure results in tests statistics with asymptotic distribution depending only on the number of common trends under the null hypothesis of rank r, and on the interval of
(erf) of the tests LR1, LR2, and LR3 and of the overall “LR1 → LR2 → LR3” testing strategy; the column “Asymptotic” reports the erf computed using the asymptotic critical
