Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Roger Moon"'
Publikováno v:
Agronomy, Vol 10, Iss 12, p 1963 (2020)
The objective of this study was to compare the forage nutritive value of cool-season perennial grasses and legumes with that of warm-season annual grasses grazed by organic dairy cows. Two pasture systems were analyzed across the grazing season at an
Externí odkaz:
https://doaj.org/article/70bc8c8b08104c569ac60b3ac20aada5
Publikováno v:
Econometrics, Vol 9, Iss 3, p 29 (2021)
Peter Phillips has had a tremendous impact on econometric theory and practice [...]
Externí odkaz:
https://doaj.org/article/36259733d5f741cb81d303bd2ec3fd31
Publikováno v:
Econometric Reviews. 40:830-851
In this paper, we investigate seemingly unrelated regression (SUR) models that allow the number of equations (N) to be large, and to be comparable to the number of the observations in each equation (T). It is well known in the literature that the con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::198915bad77b70e5676fa9c5a1b0ea52
https://doi.org/10.1080/07474938.2021.1889195
https://doi.org/10.1080/07474938.2021.1889195
Publikováno v:
Statistical Methods in Medical Research. :096228022311588
In this article, we consider a survival function estimation method that may be suitable for analyses of clinical trials of cancer treatments whose prognosis is known to be poor such as pancreatic cancer treatment. Typically, these kinds of trials are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92834174621712f77c53da06f63e0e91
https://doi.org/10.1177/09622802231158812
https://doi.org/10.1177/09622802231158812
Publikováno v:
Econometrica. 88:171-201
This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df107df72836bede3001fa82403fb6b6
https://doi.org/10.3982/ecta14952
https://doi.org/10.3982/ecta14952
We use a dynamic panel Tobit model with heteroskedasticity to generate forecasts for a large cross‐section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross‐sectional distribution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1f299fc34b2ce6857033e9dad7d563f
http://arxiv.org/abs/2110.14117
http://arxiv.org/abs/2110.14117
Publikováno v:
The Econometrics Journal. 22:262-281
Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::008ad5a5c967c16314487fad790131df
https://doi.org/10.1093/ectj/utz010
https://doi.org/10.1093/ectj/utz010
Publikováno v:
Journal of Econometrics
We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bf67f650dd494f2cff9164903cfb278
https://pubmed.ncbi.nlm.nih.gov/33100475
https://pubmed.ncbi.nlm.nih.gov/33100475
Autor:
Hyungsik Roger Moon, Khai Xiang Chiong
Publikováno v:
The Econometrics Journal. 21:247-263
Gaussian graphical models are recently used in economics to obtain networks of dependence among agents. A widely used estimator is the graphical least absolute shrinkage and selection operator (GLASSO), which amounts to a maximum likelihood estimatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d0c9933a456fd95bc4c109bf1aaa341
https://doi.org/10.1111/ectj.12104
https://doi.org/10.1111/ectj.12104