corona.bib

@book{8328358dab6746d884ee538c687aa0dd,
title = "Data Analysis using Regression and Multilevel/Hierarchical Models",
keywords = "Modèles multiniveaux (Statistique), Regressieanalyse, data analysis, Multiniveau-analyse, Matematisk statistik, Analyse de régression, Statistics as Topic, Regression analysis, Analyse statistique, Multilevel models (Statistics), Regressionsanalys, Análise de regressão e de correlação, Multivariat analys, Méthodes statistiques, Regressionsanalyse, statistical methods",
author = "Andrew Gelman and Jennifer Hill",
note = "Includes bibliographical references (pages 575-600) and indexes",
year = "2007",
language = "English (US)",
isbn = "9780521686891",
series = "Analytical methods for social research",
publisher = "Cambridge University Press",
address = "United Kingdom",

}
@article{doi:10.1198/004017005000000661,
author = {Andrew Gelman},
title = {Multilevel (Hierarchical) Modeling: What It Can and Cannot Do},
journal = {Technometrics},
volume = {48},
number = {3},
pages = {432-435},
year  = {2006},
publisher = {Taylor & Francis},
doi = {10.1198/004017005000000661},

URL = { 
        https://doi.org/10.1198/004017005000000661
    
},
eprint = { 
        https://doi.org/10.1198/004017005000000661
    
}

}
@article{doi:10.20955/es.2020.6,
author = {Bill Dupor},
title = {Possible Fiscal Policies for Rare, Unanticipated, and Severe Viral Outbreaks},
journal = {Economic Synopses}, 
volume = {6}, 
year = {2020},
doi = {10.20955/es.2020.6} 
}

@misc{grodriguez,
author={German Rodríguez},
title={Lecture Notes on Generalized Linear Models},
year={2007},
URL={http://data.princeton.edu/wws509/notes/}
}

@article{bramdeitz,
author={Jason Bram and Richard Deitz},
title={The Coronavirus Shock Looks More like a Natural Disaster than a Cyclical Downturn},
journal={Federal Reserve Bank of New York Liberty Street Economics},
month={April}, 
year={2020},
URL={https://libertystreeteconomics.newyorkfed.org/2020/04/the-coronavirus-shock-looks-more-like-a-natural-disaster-than-a-cyclical-downturn.html},
}

@MISC{rochfordlogisticutility,
author={Austin Rochford},
title={Utility Theory and Logistic Regression},
year={2015},
URL={https://austinrochford.com/posts/2015-01-12-utility-theory-logistic-regression.html}
}

@misc{groshencovid,
author={Erica Groshen},
year={2020},
month={May},
title={It Matters that Most COVID Layoffs in March were Furloughs},
URL={https://www.ilr.cornell.edu/work-and-coronavirus/public-policy/it-matters-most-covid-layoffs-march-were-furloughs}
}

@article{austin_harrell_klaveren,
author={Peter C. Austin, Frank E. Harrell Jr., David van Klaveren},
title={Graphical calibation curves and the Integrated calibration Index (ICI) for survival models},
journal={Statistics in Medicine},
month={April},
year={2020}
}

@misc{lifelines,
  author = {{Cam Davidson Pilon}},
  title = {lifelines},
  url = {https://github.com/CamDavidsonPilon/lifelines/tree/v0.24.13},
  version = {0.24.13},
  year={2020},
  month={June},
  date = {2022-06-22},
  doi = {10.5281/zenodo.3903636}
}

@article{pymc3,
author = {Salvatier J., Wiecki T. V., Fonnesbeck C.},
year={2016},
title={Probabilistic programming in Python using PyMC3},
journal={PeerJ Computer Science 2:e55},
doi={10.7717/peerj-cs.55}
}

@article{vehtari_practical_2017,
	title = {Practical {Bayesian} model evaluation using leave-one-out cross-validation and {WAIC}},
	volume = {27},
	issn = {1573-1375},
	url = {https://doi.org/10.1007/s11222-016-9696-4},
	doi = {10.1007/s11222-016-9696-4},
	abstract = {Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparison of predictive errors between two models. We implement the computations in an R package called loo and demonstrate using models fit with the Bayesian inference package Stan.},
	number = {5},
	journal = {Statistics and Computing},
	author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
	month = sep,
	year = {2017},
	pages = {1413--1432}
}