This paper focuses on survey expectations and discusses their uses for testing and
modelling of expectations. Alternative models of expectations formation are reviewed and …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The author's research has been directed towards inference involving observables rather
than parameters. In this book, he brings together his views on predictive or observable …

ABSTRACT Automated Valuation Models (AVMs) based on Machine Learning (ML)
algorithms are widely used for predicting house prices. While there is consensus in the …

Estimators and predictors that are optimal relative to Varian's asymmetric LINEX loss
function are derived for a number of well-known models. Their risk functions and Bayes risks …

This paper surveys a range of important developments in the area of preliminary‐test
inference in the context of econometric modelling. Both pre‐test estimation and pre‐test …

Preface and TOC Page 1 Copyright © FX Diebold. All rights reserved. Elements of
Forecasting Francis X. Diebold University of Pennsylvania Page 2 Fcst4-00-2 Copyright © …

This book provides a formal analysis of the models, procedures, and measures of economic
forecasting with a view to improving forecasting practice. David Hendry and Michael …

Offers a treatment of different kinds of James-Stein and ridge regression estimators from a
frequentist and Bayesian point of view. The book explains and compares estimators …

Abstract We show that Deep Neural Networks introduce two heteroscedastic Gumbel noise
sources into Q-Learning. To account for these noise sources, we propose Double Gumbel Q …