English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a method for forecasting individual outcomes and estimating randomeffects in linear panel data models and value-added models when the panel has ashort time dimension. The method is robust, trivial to implement and requiresminimal assumptions. The idea is to take a weighted average of time series- andpooled forecasts/estimators, with individual weights that are based on timeseries information. We show the forecast optimality of individual weights, bothin terms of minimax-regret and of mean squared forecast error. We then providefeasible weights that ensure good performance under weaker assumptions thanthose required by existing approaches. Unlike existing shrinkage methods, ourapproach borrows the strength - but avoids the tyranny - of the majority, bytargeting individual (instead of group) accuracy and letting the data decidehow much strength each individual should borrow. Unlike existing empiricalBayesian methods, our frequentist approach requires no distributionalassumptions, and, in fact, it is particularly advantageous in the presence offeatures such as heavy tails that would make a fully nonparametric procedureproblematic.

A Robust Method for Microforecasting and Estimation of Random Effects