Evidence map›Paper›PMID 33519026›Full record

ArticleJournal of econometrics2023

How to go viral: A COVID-19 model with endogenously time-varying parameters.

Paul Ho, Thomas A Lubik, Christian Matthes

Abstract read
In one paragraph

Article in Journal of econometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paul HoFederal Reserve Bank of Richmond, Research Department, P.O. Box 27622, Richmond, VA 23261, United States of America.
Thomas A LubikFederal Reserve Bank of Richmond, Research Department, P.O. Box 27622, Richmond, VA 23261, United States of America.
Christian MatthesIndiana University, Wylie Hall, 100 South Woodlawn Avenue, Bloomington, IN 47405, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

Indexed as

Bayesian estimationPanelTime-varying parameters

Identifiers

PMID33519026
PMCPMC7833926

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.