Evidence map›Paper›PMID 36415868›Full record

ArticleEmpirical economics2022

The response of household debt to COVID-19 using a neural networks VAR in OECD.

Emmanuel C Mamatzakis, Steven Ongena, Mike G Tsionas

Abstract read
In one paragraph

Article in Empirical economics, 2022. 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.

Emmanuel C MamatzakisDepartment of Management, Birkbeck, University of London, Malet Street, Bloomsbury, London, WC1E 7HX UK.ORCID 0000-0002-2623-0029
Steven OngenaDepartment of Banking and Finance, University of Zurich, 8032 Zurich, Switzerland.
Mike G TsionasMontpellier Business School, Lancaster University Management School, Bailrigg, Lancaster, LA1 4YX UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel VAR for OECD countries. Our model also includes a plethora of non-pharmaceutical and pharmaceutical interventions. We opt for a global neural networks panel VAR (GVAR) methodology that nests all OECD countries in the sample. Because linear factor models are unable to capture the variability in our data set, the use of an artificial neural network (ANN) method permits to capture this variability. The number of factors, as well as the number of intermediate layers, is determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. We also report

Indexed as

ANNCOVID-19Household debtMIDASOECDPanel VAR

Identifiers

PMID36415868
PMCPMC9668402

What OpenQuestion holds

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Registered trials

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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.