A theory of robust long-run variance estimation

A theory of robust long-run variance estimation

Long-run variance estimation can typically be viewed as the problem of estimating the scale of a limiting continuous time Gaussian process on the unit interval. A natural benchmark model is given by a sample that consists of equally spaced observations of this limiting process. The paper analyzes the asymptotic robustness of long-run variance estimators to contaminations of this benchmark model...

Long Run Variance Estimation Using Steep Origin Kernels without Truncation

Robust Estimation of Wavelet Variance

The wavelet variance provides a scale-based decomposition of the process variance for a time series or a random field and has been used to analyze various multi-scale processes. Examples of such processes include atmospheric pressure, deviations in time as kept by atomic clocks, soil properties in agricultural plots, snow fields in the polar regions and brightness temperature maps of South Paci...

A note on robust variance estimation for cluster-correlated data.

There is a simple robust variance estimator for cluster-correlated data. While this estimator is well known, it is poorly documented, and its wide range of applicability is often not understood. The estimator is widely used in sample survey research, but the results in the sample survey literature are not easily applied because of complications due to unequal probability sampling. This brief no...

A Theory of Disasters and Long-run Growth

We examine the long-term consequences to economic growth of disasters using a discrete-time endogenous growth model. We consider two types of hypothetical disasters: historical disasters, which follow a Bernoulli process, and periodic disasters, which are taken as a regular event by assuming that one period is a sufficient time period. We show that the effects of historical disasters on the ste...