Long-Range Dependent Curve Time Series

By Degui Li, Peter M. Robinson, Han Lin Shang. Journal of the American Statistical Association, 2020

107 citations recorded by the source corpus.

Abstract We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of the curves. In a semiparametric context, we propose an estimate of the memory parameter and establish its consistency. A Monte Carlo study of finite-sample performance is included, along with two empirical applications. The first of these finds a degree of stability and persistence in intraday stock returns. The second finds similarity in the extent of long memory in incremental age-specific fertility rates across some developed nations. Supplementary materials for this article are available online.

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6d3ac4c3d2782732e9fada1eb03df1a10d497183

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

In Economics and Econometrics

In Finance

Citations in the corpus

Forecasting: theory and practice (2020, background): “points to the extent they have to reach an agreement on a common forecast (Litsiou et al., 2019). Sniezek and Henry (1989) found that members of interacting groups provide more accurate judgments compared to individuals.”