Threshold effects of financial stress on monetary policy rules: A panel data analysis

Floro, Danvee; Roye, Björn van

doi:10.1016/j.iref.2017.07.023

Cited by 25 publications

(14 citation statements)

References 46 publications

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“…Finally, we envisage the possibility of the existence of a cross-sectional dependence in the error terms of the model (2). As a final robustness check, we use a factor-augmented dynamic panel threshold regression model with estimated common components to deal with cross-sectional dependence (Floro and van Roye, 2017). Indeed, in the dynamic panel threshold regression literature, error cross-sectional dependencies across countries are most of the time neglected.…”

Section: Robustness Checkmentioning

confidence: 99%

“…Indeed, in the dynamic panel threshold regression literature, error cross-sectional dependencies across countries are most of the time neglected. Yet, the presence of cross-sectional dependence can lead to biased estimators, consistency problems or spurious regression (Phillips and Sul, 2003;Bai, 2009 Moreover, assuming independence could make one believe that there are threshold effects or non-linear effects of health on economic growth while it may not be the case.We usePesaran (2004)'s cross-sectional dependence test on the residuals the dynamic panel threshold (model 2), as in Floro and van Roye (2017). The result of the test is presented in Table 7.…”

Section: Robustness Checkmentioning

confidence: 99%

“…The author argued that this method is a quick and effective approach of extracting common factors. In fact, the common correlated coefficients estimator (CCE) approach proposed by Pesaran (2006) requires an a priori value of the threshold variable, which would not be suitable in our analysis given that our threshold values are determined endogenously (Floro and van Roye, 2017). The question that arises now is the number of factors than should be incorporated in the regression.…”

mentioning

confidence: 99%

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Threshold Effects of Health on Economic Growth in Sub-Saharan African Countries: Evidence from a Dynamic Panel Threshold Model

Kouton¹,

N’Guessan²,

Ayivodji³

2018

JEDS

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This paper investigates the presence of threshold effects in the health-economic growth nexus for 24 Sub-Saharan Africa countries. We apply a dynamic panel threshold model and find that health human capital stock, measured by life expectancy at birth, has a positive and significant impact on economic growth. This impact becomes more important as health investments, captured by public health expenditures, increase. We estimate a threshold of 3.5% of the ratio public health expenditures/GDP above which life expectancy at birth has a higher impact on economic growth. This may be due to the fact that even if life expectancy is increased, individuals will not be productive unless they are able to maintain and improve their health status. We recommend investments in health in order to make drugs purchase much easier. More investments should be devoted to fight malaria, HIV/AIDS and diseases with a high morbidity that reduce the productivity of individuals.

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Section: Robustness Checkmentioning

confidence: 99%

Section: Robustness Checkmentioning

confidence: 99%

mentioning

confidence: 99%

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Threshold Effects of Health on Economic Growth in Sub-Saharan African Countries: Evidence from a Dynamic Panel Threshold Model

Kouton¹,

N’Guessan²,

Ayivodji³

2018

JEDS

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“…As proven by existing literature, urban spatial agglomeration can bring about agglomeration economies. [30][31][32] First, the model needed to set the threshold variable and the indicator function. 27,28 Therefore, to effectively analyze the different effects of various stages of urban spatial structure on urban energy intensity, this paper adopts the threshold panel analysis model.…”

Section: Threshold Panel Modelmentioning

confidence: 99%

“…29 This model can effectively classify the core variables and their phased effects on dependent variables, which has been successfully used in many fields of research. [30][31][32] First, the model needed to set the threshold variable and the indicator function. The choice of the threshold variable was based on both theoretical and empirical bases.…”

Section: Threshold Panel Modelmentioning

confidence: 99%

Decrease in urban energy intensity: Is there a role for urban spatial structure?

Liu

Long

2019

Energy Science & Engineering

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Using satellite remote sensing data and the threshold panel model, this research explores the impact of urban spatial structure on urban energy intensity. The evaluation index of urban spatial structure includes compactness and elongation rate. Results from empirical studies of 30 cities in China from 1996 to 2016 indicate that urban spatial structure has a significant impact on urban energy intensity. There is a positive correlation between urban spatial structure elongation rate and urban energy intensity. However, with the increase in urban fixed assets investment, there is a negative correlation between elongation rate and energy intensity, which implies an inverted‐U relationship. The results also indicate that urban compactness is negatively correlated with urban energy intensity. Therefore, proper management of urban spatial structure can effectively promote the reduction in urban energy intensity. The elongation of urban suburb space and the compactness with the internal space of the city can be developed simultaneously. While playing the role of a spatial agglomeration economy, the negative effects of over agglomeration are avoided, which jointly promote the reduction in urban energy intensity.

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The effect of financial stress on renewable energy consumption: evidence from US data

Miah,

Shafiullah,

Alam

2023

Environ Dev Sustain

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Threshold effects of financial stress on monetary policy rules: A panel data analysis

Cited by 25 publications

References 46 publications

Threshold Effects of Health on Economic Growth in Sub-Saharan African Countries: Evidence from a Dynamic Panel Threshold Model

Threshold Effects of Health on Economic Growth in Sub-Saharan African Countries: Evidence from a Dynamic Panel Threshold Model

Decrease in urban energy intensity: Is there a role for urban spatial structure?

The effect of financial stress on renewable energy consumption: evidence from US data

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