Wei Wei

Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study

Publisher: Elsevier BV

Date: 10-2020

DOI: 10.1016/S0140-6736(20)30750-9

Comments on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference

Publisher: Oxford University Press (OUP)

Date: 03-2016

DOI: 10.1093/JJFINEC/NBV012

The Geometric-VaR Backtesting Method

Publisher: Oxford University Press (OUP)

Date: 14-07-2015

DOI: 10.1093/JJFINEC/NBV015

A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method

Publisher: Elsevier BV

Date: 05-2018

DOI: 10.1016/J.ENECO.2018.03.037

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

Publisher: Springer Science and Business Media LLC

Date: 20-04-2020

DOI: 10.1038/S41591-020-0807-6

Abstract: A double burden of malnutrition occurs when in iduals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of % in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic.

Identifying Risk Factors and Their Premia: A Study on Electricity Prices

Publisher: Oxford University Press (OUP)

Date: 24-06-2022

DOI: 10.1093/JJFINEC/NBAC019

Abstract: Risk premia are difficult to identify in nonstorable commodities such as electricity. In this article, we propose a modified Fama–French regression framework and show that when the spot prices do not follow a martingale—a common assumption in the electricity market—model specifications play an important role in detecting time-varying risk premia in the futures market. With this insight, we propose a multi-factor model that captures important dynamics in electricity prices and an estimation method based on particle Markov chain Monte Carlo to separate risk factors in energy prices. Using spot and futures data in the Germany/Austria electricity market, we demonstrate that our proposed model surpasses alternative models that ignore some of risk factors in forecasting spot prices and in detecting time-varying risk premia. Based on our proposed model, we separately identify risk premia carried by in idual risk factors and document large variations in the premia of each factor.

A Stochastic Price Duration Model for Estimating High-Frequency Volatility

Publisher: Oxford University Press (OUP)

Date: 23-10-2023

DOI: 10.1093/JJFINEC/NBAD029

Monash University

Location: Australia

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Aarhus Universitet Business And Social Sciences

Location: Denmark

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North Carolina State University

Location: United States of America

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Monash University - Caulfield Campus

Location: Australia

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Discovery Projects - Grant ID: DP200102769

Start Date: 08-2020

End Date: 12-2024

Amount: $518,877.00

Funder: Australian Research Council