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I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters to mix over different frequencies of parameter changes in a data driven way and permits great flexibility while achieving model parsimony. A convenient strategy is discussed to infer if each coefficient is static or dynamic and, if dynamic, how frequent the parameter change is. An MCMC scheme is developed for model estimation. The performance of the proposed approach is illustrated in studies of both simulated and real economic data. [ABSTRACT FROM AUTHOR]

We consider robust inference for an autoregressive parameter in a stationary linear autoregressive model with GARCH innovations. As the innovations exhibit GARCH, they are by construction heavy-tailed with some tail index κ. This implies that the rate of convergence as well as the limiting distribution of the least squares estimator depend on κ. In the spirit of Ibragimov and Müller ("t-statistic based correlation and heterogeneity robust inference", Journal of Business & Economic Statistics, 2010, vol. 28, pp. 453–468), we consider testing a hypothesis about a parameter based on a Student's t-statistic based on least squares estimates for a fixed number of groups of the original sample. The merit of this approach is that no knowledge about the value of κ nor about the rate of convergence and the limiting distribution of the least squares estimator is required. We verify that the two-sided t-test is asymptotically a level α test whenever α ≤ 5 % for any κ ≥ 2 , which includes cases where the innovations have infinite variance. A simulation experiment suggests that the finite-sample properties of the test are quite good. [ABSTRACT FROM AUTHOR]

A common feature of financial time series is their strong persistence. Yet, long memory may just be the spurious effect of either structural breaks or slow switching regimes. We explore the effects of spurious long memory on the elasticity of the stock market price with respect to volatility and show how cross-sectional aggregation may generate spurious persistence in the data. We undertake an extensive Monte Carlo study to compare the performance of five tests, constructed under the null of true long memory versus the alternative of spurious long memory due to level shifts or breaks. [ABSTRACT FROM AUTHOR]