To Pool or Not to Pool: Revisited

Pesaran, M. Hashem; Zhou, Qiankun

doi:10.1111/obes.12220

Cited by 12 publications

(15 citation statements)

References 9 publications

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“…Even though there are significant differences in meteorological conditions between our set of countries, they were all located in a relatively homogeneous North Hemisphere (except Australia, Chile and New Zealand). Thus, all conditions worked with a suitable econometric panel; unobserved heterogeneity across countries was included inside a relative homogeneous panel [see the "to pool or not pool debate" developed by Pesaran et al [ 32 ]].…”

Section: Econometric Frameworkmentioning

confidence: 99%

Meteorological factors against COVID-19 and the role of human mobility

2021

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In the vein of recent empirical literature, we reassessed the impact of weather factors on Covid-19 daily cases and fatalities in a panel of 37 OECD countries between 1st January and 27th July 2020. We considered five different meteorological factors. For the first time, we used a dynamic panel model and considered two different kinds of channels between climate and Covid-19 virus: direct/physical factors related to the survival and durability dynamics of the virus on surfaces and outdoors and indirect/social factors through human behaviour and individual mobility, such as walking or driving outdoors, to capture the impact of weather on social distancing and, thus, on Covid-19 cases and fatalities. Our work revealed that temperature, humidity and solar radiation, which has been clearly under considered in previous studies, significantly reduce the number of Covid-19 cases and fatalities. Indirect effects through human behaviour, i.e., correlations between temperature (or solar radiation) and human mobility, were significantly positive and should be considered to correctly assess the effects of climatic factors. Increasing temperature, humidity or solar radiation effects were positively correlated with increasing mobility effects on Covid-19 cases and fatalities. The net effect from weather on the Covid-19 outbreak will, thus, be the result of the physical/direct negative effect of temperature or solar radiation and the mobility/indirect positive effect due to the interaction between human mobility and those meterological variables. Reducing direct effects of temperature and solar radiation on Covid-19 cases and fatalities, when they were significant, were partly and slightly compensated for positive indirect effects through human mobility. Suitable control policies should be implemented to control mobility and social distancing even when the weather is favourable to reduce the spread of the Covid-19 virus.

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Section: Econometric Frameworkmentioning

confidence: 99%

Meteorological factors against COVID-19 and the role of human mobility

2021

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“…Concerning the climatic factors, even though there exist significant differences in meteorological conditions between our set of countries, they are all located in a relative homogeneous North Hemisphere. We thus have all conditions to work with a suitable panel: unobserved heterogeneity across countries is included inside a relative homogeneity panel (see the “to pool or not pool debate”, for instance in (31)).…”

Section: Methodsmentioning

confidence: 99%

Is climate a curse or a bless in the Covid-19 virus fighting?

Damette

Mathonnat

Goutte

2020

Preprint

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Faced with the global pandemic of Covid-19, we need to better understand the links between meteorological factors and the virus and investigate the existence of potential seasonal patterns. In the vein of a recent empirical literature, we reassess the impact of weather factors on Covid-19 daily cases in a panel of advanced and emerging countries between January the first and 28th May 2020. We consider 5 different meteorological factors and go further previous studies. In addition, we give a short-run and medium/long-run time perspective of the dramatic outcomes of the pandemic by both considering infected people (short-run) and fatalities (long-run). Our results reveal that the choice of delays and time perspective of the effects of climatic factors on the virus are crucial as well as Covid-19 outcomes can explain the discrepancies in the previous literature. For the first time, we use a dynamic panel model and consider two different kinds of channels between climate and Covid-19 virus: 1) direct/physical factors related to the survivals and durability dynamics of the virus in surfaces and outdoors and 2) an indirect factor through human behaviors and individual mobility - walking or driving outdoors - to capture the impact of climate on social distancing and thus on Covid-19 outcomes. Our model is estimated \emph{per se} two different estimators and persistence, delays in patterns, nonlinearities and numerous specifications changes are taken into account with many robustness checks. Our work reveal that temperatures and, more interestingly, solar radiation - that has been clearly undervalued in previous studies - are significant climatic drivers on Covid-19 outbreak. Indirect effects through human behaviors ie interrelationships between climatic variables and people mobility are significantly positive and should be considered to correctly assess the effects of climatic factors. Since climate is per se purely exogenous, climate tend to strengthen the effect of mobility on virus spread. The net effect from climate on Covid-19 outbreak will thus result from the direct negative effect of climatic variables and from the indirect effect due to the interaction between mobility and them. Direct negative effects from climatic factors on Covid-19 outcomes - when they are significant - are partly compensated by positive indirect effects through human mobility. Suitable control policies should be implemented to control the mobility and social distancing. \\

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“…α r measures the degree of heterogeneity with α r = 1 representing the highest degree of heterogeneity. A similar idea is used by Pesaran and Zhou (2018) in their analysis of poolability in panel data models.…”

Section: Remarkmentioning

confidence: 98%

Identification and Estimation of Categorical Random Coefficient Models

Gao¹,

Pesaran²

2023

Preprint

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This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of Moments estimation procedure is proposed also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we find that moments of the random coefficients can be estimated reasonably accurately, but large samples are required for estimation of the parameters of the underlying categorical distribution. The utility of the proposed estimator is illustrated by estimating the distribution of returns to education in the U.S. by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within group heterogeneity has been rising mostly in the case of individuals with high school or less education.

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To Pool or Not to Pool: Revisited

Cited by 12 publications

References 9 publications

Meteorological factors against COVID-19 and the role of human mobility

Meteorological factors against COVID-19 and the role of human mobility

Is climate a curse or a bless in the Covid-19 virus fighting?

Identification and Estimation of Categorical Random Coefficient Models

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