Evidence map›Paper›PMID 36454731›Full record

ArticlePloS one2022

COVID-19: Tail risk and predictive regressions.

Walter Distaso, Rustam Ibragimov, Alexander Semenov, Anton Skrobotov

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Power law in COVID-19 cases in China.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022
    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

4 authors.

Walter DistasoImperial College London, Business School, London, United Kingdom.
Rustam IbragimovImperial College London, Business School, London, United Kingdom.
Alexander SemenovCenter for Econometrics and Business Analytics, St. Petersburg State University, St. Petersburg, Russia.ORCID 0000-0003-2691-4575
Anton SkrobotovCenter for Econometrics and Business Analytics, St. Petersburg State University, St. Petersburg, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

Indexed as

COVID-19AsiaEuropeHumansPandemicsTime Factors

Identifiers

PMID36454731
PMCPMC9714707

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.